Building system with a dynamic space graph with temporary relationships

ABSTRACT

A building system including one or more memory devices configured to store instructions thereon, the instructions causing one or more processors to generate a temporary relationship between a first entity and a second entity of a space graph, cause the space graph to include the temporary relationship, perform one or more control operations based on the space graph including the temporary edge, receive new building data from the one or more building data sources, determine whether to generate a permanent relationship to replace the temporary relationship based on the new building data, and update the space graph by causing the permanent relationship to replace the temporary relationship of the space graph in response to a determination to generate the permanent relationship to replace the temporary relationship by causing a permanent edge to replace the temporary edge.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/688,819 filed Nov. 19, 2019, which is a continuation of U.S. patentapplication Ser. No. 16/260,078 filed Jan. 28, 2019 (now U.S. Pat. No.10,505,756) which is a continuation-in-part of U.S. patent applicationSer. No. 16/048,052 filed Jul. 27, 2018 (now U.S. Pat. No. 10,417,451),which claims the benefit of and priority to U.S. Provisional PatentApplication No. 62/564,247 filed Sep. 27, 2017, U.S. Provisional PatentApplication No. 62/611,984 filed Dec. 29, 2017, and U.S. ProvisionalPatent Application No. 62/611,974 filed Dec. 29, 2017. U.S. patentapplication Ser. No. 16/260,078 filed Jan. 28, 2019 (now U.S. Pat. No.10,505,756) is also a continuation-in-part of U.S. patent applicationSer. No. 16/142,578 filed Sep. 26, 2018, which claims the benefit of andpriority to U.S. Provisional Patent Application No. 62/564,247 filedSep. 27, 2017, and U.S. Provisional Patent Application No. 62/612,167filed Dec. 29, 2017. U.S. patent application Ser. No. 16/142,578 filedSep. 26, 2018 is a continuation-in-part of U.S. patent application Ser.No. 15/644,519 filed Jul. 7, 2017 (now U.S. Pat. No. 10,095,756), whichclaims the benefit of and priority to U.S. Provisional PatentApplication No. 62/457,654 filed Feb. 10, 2017. U.S. patent applicationSer. No. 16/142,578 filed Sep. 26, 2018 is also a continuation-in-partof U.S. patent application Ser. No. 15/644,581 filed Jul. 7, 2017 (nowU.S. Pat. No. 10,169,486), which claims the benefit of and priority toU.S. Provisional Patent Application No. 62/457,654 filed Feb. 10, 2017.U.S. patent application Ser. No. 16/142,578 filed Sep. 26, 2018 is alsoa continuation-in-part of U.S. patent application Ser. No. 15/644,560filed Jul. 7, 2017 (now U.S. Pat. No. 10,417,245), which claims thebenefit of and priority to U.S. Provisional Patent Application No.62/457,654 filed Feb. 10, 2017. U.S. patent application Ser. No.16/260,078 filed Jan. 28, 2019 (now U.S. Pat. No. 10,505,756) is also acontinuation-in-part of U.S. patent application Ser. No. 16/142,758filed Sep. 26, 2018 which claims the benefit of and priority to U.S.Provisional Patent Application No. 62/564,247 filed Sep. 27, 2017, U.S.Provisional Patent Application No. 62/588,179 filed Nov. 17, 2017, U.S.Provisional Patent Application No. 62/588,190 filed Nov. 17, 2017, U.S.Provisional Patent Application No. 62/588,114 filed Nov. 17, 2017, andU.S. Provisional Patent Application No. 62/611,962 filed Dec. 29, 2017.U.S. patent application Ser. No. 16/260,078 filed Jan. 28, 2019 (nowU.S. Pat. No. 10,505,756) is also a continuation-in-part of U.S. patentapplication Ser. No. 16/036,685 filed Jul. 16, 2018, which claims thebenefit of and priority to U.S. Provisional Patent Application No.62/533,581 filed Jul. 17, 2017. The entirety of each of these patentapplications is incorporated by reference herein.

BACKGROUND

The present disclosure relates generally to a building management systemand more particularly to building information management of a buildingmanagement system that collects, manages, and utilizes data forinterconnected devices and other entities.

In a typical building management systems, relationships between spaces,assets, and/or people are usually pre-defined and generally have ahierarchical organization. This can lead to many deficiencies. Forexample, any change in the relationships between spaces, assets, and/orpeople may require underlying databases and/or programs of the buildingmanagement system to reconfigure the relationships with external userintervention. This does not allow the BMS to naturally and automaticallyadapt to changes in usage, systems, and/or operating conditions.Furthermore, this also requires specific programming paradigms to beinbuilt into the building management system to allow for these changes.The exception handling scenarios in building management system for anydeviation from expected semantic interpretation of data around spaces,assets, and/or people can become cumbersome and complicated, oftenleading to downstream erroneous data transmission and informationprocessing. The prevalent approach based on linear associations also donot allow for multi-dimensional dynamic and simultaneous analysis ofbuilding information around spaces, assets, and/or people for differenttypes of analysis and simulation insights. The increasing usage ofartificial intelligence influenced analytical techniques which are notbased on heuristics require a more flexible organization of information.

SUMMARY

One implementation of the present disclosure is a building system foroperating a building and managing building information, the buildingsystem including one or more memory devices configured to storeinstructions thereon, the instructions causing one or more processors toreceive building data from one or more building data sources, generaterelationships between entities based on the building data, wherein therelationships includes a pair of relationships between a first entityand a second entity of the entities representing two different types ofrelationships, wherein the pair of relationships includes a firstrelationship between the first entity and the second entity and a secondrelationship between the second entity and the first entity, and updatea space graph by causing the space graph to store nodes representing theentities and edges between the nodes representing the relationships,wherein the space graph is a graph data structure.

In some embodiments, the instructions cause the one or more processorsto ingest data values of the building data into the space graph, thedata values associated with the entities and perform one or moreoperations with the space graph based on both the relationships of theentities and the ingested data values.

In some embodiments, the instructions cause the one or more processorsto receive new building data from the one or more building data sources,generate, based on the new building data, a new relationship between thefirst entity and the second entity, and update the space graph with thenew relationship by causing the space graph to store a new edge betweena first node of the nodes representing the first entity and a secondnode of the nodes representing the second entity.

In some embodiments, the instructions cause the one or more processorsto receive a query for information of the space graph from a requestingdevice, wherein the information is included by one of the nodes of thespace graph, retrieve the information from the space graph by traversingat least some of the entities and at least some of the edges to identifythe information without traversing other entities or other relationshipsof a data structure other than the space graph, and provide theinformation to the requesting device.

In some embodiments, the query includes an indication of the at leastsome of the nodes and the at least some of the entities to traverse toidentify the information.

In some embodiments, the instructions cause the one or more processorsto generate a control algorithm based on the space graph and operate oneor more pieces of building equipment based on the control algorithm.

In some embodiments, the instructions cause the one or more processorsto receive new building data from the one or more building data sources,generate a new relationship between the first entity and the secondentity, update the space graph with the new relationship by causing thespace graph to store a new edge between a first node of the nodesrepresenting the first entity and a second node of the nodesrepresenting the second entity, and update the control algorithm basedon the new edge of the updated space graph and operate the one or morepieces of building equipment based on the updated control algorithm.

In some embodiments, the instructions cause the one or more processorsto receive new building data from the one or more building data sources,generate a temporary relationship between the first entity and thesecond entity based on the new building data, cause the space graph toinclude the temporary relationship by storing a temporary edge between afirst node of the nodes representing the first entity and a second nodeof the nodes representing the second entity, and perform one or morecontrol operations based on the space graph including the temporaryedge.

In some embodiments, the instructions cause the one or more processorsto receive additional new building data from the one or more buildingdata sources, the additional new building data based on the one or morecontrol operations based on the space graph including the temporaryedge, determine whether to generate a formal relationship to replace thetemporary relationship based on the new building data, and update thespace graph by causing the formal relationship to replace the temporaryrelationship of the space graph in response to a determination togenerate the formal relationship to replace the temporary relationshipby causing a formal edge to replace the temporary edge.

In some embodiments, the instructions cause the one or more processorsto receive new building data from the one or more building data sources,identify, based on the building data, an indirect relationship betweenthe first entity and the second entity of a space graph, the indirectrelationship caused by a control algorithm of the space graph, andupdate the space graph with the indirect relationship by causing thespace graph to include an indirect relationship edge between a firstnode of the nodes representing the first entity and a second node of thenodes representing the second entity.

In some embodiments, the instructions cause the one or more processorsto update the control algorithm of the space graph based on the indirectrelationship edge and operate one or more pieces of building equipmentbased on the updated control algorithm.

Another implementation of the present disclosure is a method for abuilding system. The method includes receiving, by a processing circuit,building data from one or more building data sources, generating, by theprocessing circuit, relationships between entities based on the buildingdata, wherein the relationships includes a pair of relationships betweena first entity and a second entity of the entities representing twodifferent types of relationships, wherein the pair of relationshipsincludes a first relationship between the first entity and the secondentity and a second relationship between the second entity and the firstentity, and updating, by the processing circuit, a space graph bycausing the space graph to store nodes representing the entities andedges between the nodes representing the relationships, wherein thespace graph is a graph data structure.

In some embodiments, the method includes ingesting, by the processingcircuit, data values of the building data into the space graph, the datavalues associated with the entities and performing, by the processingcircuit, one or more operations with the space graph based on both therelationships of the entities and the ingested data values.

In some embodiments, the method includes receiving, by the processingcircuit, new building data from the one or more building data sources,generating, by the processing circuit, based on the new building data, anew relationship between the first entity and the second entity, andupdating, by the processing circuit, the space graph with the newrelationship by causing the space graph to store a new edge between afirst node of the nodes representing the first entity and a second nodeof the nodes representing the second entity.

In some embodiments, the method includes receiving, by the processingcircuit, a query for information of the space graph from a requestingdevice, wherein the information is included by one of the nodes of thespace graph, retrieving, by the processing circuit, the information fromthe space graph by traversing at least some of the entities and at leastsome of the edges to identify the information without traversing otherentities or other relationships of a data structure other than the spacegraph, and providing, by the processing circuit, the information to therequesting device.

In some embodiments, the query includes an indication of the at leastsome of the nodes and the at least some of the entities to traverse toidentify the information.

In some embodiments, the method includes generating, by the processingcircuit, a control algorithm based on the space graph and operate one ormore pieces of building equipment based on the control algorithm.

In some embodiments, the method includes receiving, by the processingcircuit, new building data from the one or more building data sources,generating, by the processing circuit, a new relationship between thefirst entity and the second entity, updating, by the processing circuit,the space graph with the new relationship by causing the space graph tostore a new edge between a first node of the nodes representing thefirst entity and a second node of the nodes representing the secondentity, and updating, by the processing circuit, the control algorithmbased on the new edge of the updated space graph and operate the one ormore pieces of building equipment based on the updated controlalgorithm.

In some embodiments, the method includes receiving, by the processingcircuit, new building data from the one or more building data sources,generating, by the processing circuit, a temporary relationship betweenthe first entity and the second entity based on the new building data,causing, by the processing circuit, the space graph to include thetemporary relationship by storing a temporary edge between a first nodeof the nodes representing the first entity and a second node of thenodes representing the second entity, and performing, by the processingcircuit, one or more control operations based on the space graphincluding the temporary edge.

In some embodiments, the method includes receiving, by the processingcircuit, additional new building data from the one or more building datasources, the additional new building data based on the one or morecontrol operations based on the space graph including the temporaryedge, determining, by the processing circuit, whether to generate aformal relationship to replace the temporary relationship based on thenew building data, and updating, by the processing circuit, the spacegraph by causing the formal relationship to replace the temporaryrelationship of the space graph in response to a determination togenerate the formal relationship to replace the temporary relationshipby causing a formal edge to replace the temporary edge.

In some embodiments, the method includes receiving, by the processingcircuit, new building data from the one or more building data sources;identifying, by the processing circuit, based on the building data, anindirect relationship between the first entity and the second entity ofa space graph, the indirect relationship caused by a control algorithmof the space graph; and updating, by the processing circuit, the spacegraph with the indirect relationship by causing the space graph toinclude an indirect relationship edge between a first node of the nodesrepresenting the first entity and a second node of the nodesrepresenting the second entity.

In some embodiments, the method includes updating, by the processingcircuit, the control algorithm of the space graph based on the indirectrelationship edge and operating, by the processing circuit, one or morepieces of building equipment based on the updated control algorithm.

Another implementation of the present disclosure is a buildingmanagement system for operating a building and managing buildinginformation, the building management system including one or more memorydevices configured to store instructions thereon one or more processors.The one or more processors are configured to execute the instructions toreceive building data from one or more building data sources, generaterelationships between entities based on the building data, wherein therelationships includes a pair of relationships between a first entityand a second entity of the entities representing two different types ofrelationships, wherein the pair of relationships includes a firstrelationship between the first entity and the second entity and a secondrelationship between the second entity and the first entity, update aspace graph by causing the space graph to store nodes representing theentities and edges between the nodes representing the relationships,wherein the space graph is a graph data structure, ingest data values ofthe building data into the space graph, the data values associated withthe entities, and perform one or more operations with the space graphbased on both the relationships of the entities and the ingested datavalues.

In some embodiments, the one or more processors are configured toexecute the instructions to receive new building data from the one ormore building data sources, generate, based on the new building data, anew relationship between the first entity and the second entity, andupdate the space graph with the new relationship by causing the spacegraph to store a new edge between a first node of the nodes representingthe first entity and a second node of the nodes representing the secondentity.

In some embodiments, the one or more processors are configured toexecute the instructions to receive a query for information of the spacegraph from a requesting device, wherein the information is included byone of the nodes of the space graph, retrieve the information from thespace graph by traversing at least some of the entities and at leastsome of the edges to identify the information without traversing otherentities or other relationships of a data structure other than the spacegraph, and provide the information to the requesting device.

In some embodiments, the query includes an indication of the at leastsome of the nodes and the at least some of the entities to traverse toidentify the information.

In some embodiments, the one or more processors are configured toexecute the instructions to generate a control algorithm based on thespace graph and operate one or more pieces of building equipment basedon the control algorithm.

In some embodiments, the one or more processors are configured toexecute the instructions to receive new building data from the one ormore building data sources, generate a new relationship between thefirst entity and the second entity, update the space graph with the newrelationship by causing the space graph to store a new edge between afirst node of the nodes representing the first entity and a second nodeof the nodes representing the second entity, and update the controlalgorithm based on the new edge of the updated space graph and operatethe one or more pieces of building equipment based on the updatedcontrol algorithm.

Another implementation of the present disclosure is an informationmanagement system including a processing circuit configured to receivebuilding data from one or more building data sources, generate arelationships between entities based on the building data, wherein therelationships includes a pair of relationships between a first entityand a second entity of the entities representing two different types ofrelationships, wherein the pair of relationships includes a firstrelationship between the first entity and the second entity and a secondrelationship between the second entity and the first entity, and updatea space graph by causing the space graph to store nodes representing theentities and edges between the nodes representing the relationships,wherein the space graph is a graph data structure.

In some embodiments, processing circuit is configured to ingest datavalues of the building data into the space graph, the data valuesassociated with the entities and perform one or more operations with thespace graph based on both the relationships of the entities and theingested data values.

Space Graph with Multiple Relationships Between Entities

Another implementation of the present disclosure is a building systemfor operating a building and managing building information, the buildingsystem including one or more memory devices configured to storeinstructions thereon, the instructions causing one or more processors toreceive building data from one or more building data sources, generaterelationships between entities based on the building data, wherein therelationships includes a pair of relationships between a first entityand a second entity of the entities representing two different types ofrelationships, wherein the pair of relationships includes a firstrelationship between the first entity and the second entity and a secondrelationship between the second entity and the first entity, and updatea space graph by causing the space graph to store nodes representing theentities and edges between the nodes representing the relationships,wherein the space graph is a graph data structure.

In some embodiments, the first relationship indicates that the firstentity has a location within the second entity. In some embodiments,wherein the second relationship indicates that the second entitycontains the first entity.

In some embodiments, the first relationship indicates a location of thefirst entity relative to the second entity. In some embodiments, whereinthe second relationship indicates a location of the second entityrelative to the first entity.

In some embodiments, the first relationship indicates that the firstentity controls an environmental condition for the second entity. Insome embodiments, wherein the second relationship indicates that thesecond entity contains the first entity.

In some embodiments, the first entity is an agent configured to performartificial intelligence to operate the building. In some embodiments,the first relationship indicates that the agent controls the secondentity. In some embodiments, the second relationship indicates that thesecond entity is assigned to the agent.

In some embodiments, the first entity is a data point, wherein thesecond entity is a device controlled based on the data point. In someembodiments, wherein the first relationship indicates that the datapoint controls the device. In some embodiments, the second relationshipindicates that the device is operated based on the data point.

In some embodiments, the entities are each one of a space, a device, avirtual point, or an agent configured to perform artificialintelligence.

In some embodiments, the instructions cause the one or more processorsto receive a query for information of the space graph from a requestingdevice, wherein the information is included by one of the nodes of thespace graph, retrieve the information from the space graph by traversingat least some of the entities and at least some of the edges to identifythe information without traversing other entities or other relationshipsof a data structure other than the space graph, and provide theinformation to the requesting device.

In some embodiments, the query includes an indication of the at leastsome of the nodes and the at least some of the entities to traverse toidentify the information.

Another implementation of the present disclosure is a method foroperating a building, the method including receiving, by a processingcircuit, building data from one or more building data sources,generating, by the processing circuit, relationships between entitiesbased on the building data, wherein the relationships includes a pair ofrelationships between a first entity and a second entity of the entitiesrepresenting two different types of relationships, wherein the pair ofrelationships includes a first relationship between the first entity andthe second entity and a second relationship between the second entityand the first entity, and updating, by the processing circuit, a spacegraph by causing the space graph to store nodes representing theentities and edges between the nodes representing the relationships,wherein the space graph is a graph data structure.

In some embodiments, the first relationship indicates that the firstentity has a location within the second entity. In some embodiments, thesecond relationship indicates that the second entity contains the firstentity.

In some embodiments, the first relationship indicates a location of thefirst entity relative to the second entity. In some embodiments, thesecond relationship indicates a location of the second entity relativeto the first entity.

In some embodiments, the first relationship indicates that the firstentity controls an environmental condition for the second entity. Insome embodiments, the second relationship indicates that the secondentity contains the first entity.

In some embodiments, the first entity is an agent configured to performartificial intelligence to operate the building. In some embodiments,the first relationship indicates that the agent controls the secondentity. In some embodiments, wherein the second relationship indicatesthat the second entity is assigned to the agent.

In some embodiments, the first entity is a data point. In someembodiments, wherein the second entity is a device controlled based onthe data point. In some embodiments, the first relationship indicatesthat the data point controls the device. In some embodiments, the secondrelationship indicates that the device is operated based on the datapoint.

In some embodiments, the entities are each one of a space, a device, avirtual point, or an agent configured to perform artificialintelligence.

In some embodiments, the instructions cause the one or more processorsto receive a query for information of the space graph from a requestingdevice, wherein the information is included by one of the nodes of thespace graph, retrieve the information from the space graph by traversingat least some of the entities and at least some of the edges to identifythe information without traversing other entities or other relationshipsof a data structure other than the space graph, and provide theinformation to the requesting device.

In some embodiments, the query includes an indication of the at leastsome of the nodes and the at least some of the entities to traverse toidentify the information.

Another implementation of the present disclosure is a buildingmanagement system for operating a building and managing buildinginformation, the building management system including one or more memorydevices configured to store instructions thereon and one or moreprocessors configured to execute the instructions to receive buildingdata from one or more building data sources, generate relationshipsbetween entities based on the building data, wherein the relationshipsincludes a pair of relationships between a first entity and a secondentity of the entities representing two different types ofrelationships, wherein the pair of relationships includes a firstrelationship between the first entity and the second entity and a secondrelationship between the second entity and the first entity, and updatea space graph by causing the space graph to store nodes representing theentities and edges between the nodes representing the relationships,wherein the space graph is a graph data structure.

In some embodiments, the one or more processors are configured toexecute the instructions to receive a query for information of the spacegraph from a requesting device, wherein the information is included byone of the nodes of the space graph, retrieve the information from thespace graph by traversing at least some of the entities and at leastsome of the edges to identify the information without traversing otherentities or other relationships of a data structure other than the spacegraph, and provide the information to the requesting device.

Space Graph with Entity Relationships and Ingested Data

Another implementation of the present disclosure is a building systemfor operating a building and managing building information, the buildingsystem including one or more memory devices configured to storeinstructions thereon, the instructions causing one or more processors toreceive building data from one or more building data sources, generate aspace graph based on the building data, wherein the space graph is agraph data structure including nodes representing entities and aplurality edges between the entities representing relationships betweenthe entities, ingest data values of the building data into the spacegraph, the data values associated with the entities, and perform one ormore operations with the space graph based on both the relationships ofthe entities and the ingested data values.

In some embodiments, the entities include one or more agents, whereinthe one or more agents are configured to generate one or more controldecisions by querying the space graph for information, wherein theinformation includes at least some of the entities, at least some of therelationships, and at least some of the ingested data values and causeone or more pieces of equipment to operate based on the one or morecontrol decisions.

In some embodiments, the space graph is a digital twin of the building,wherein the entities represent at least one of spaces, people, ordevices associated with the building.

In some embodiments, the instructions cause the one or more processorsto receive a query for information of the space graph from a requestingdevice, wherein the information is included by one of the nodes of thespace graph, retrieve the information from the space graph by traversingat least some of the entities and at least some of the edges to identifythe information without traversing other entities or other relationshipsof a data structure other than the space graph, and provide theinformation to the requesting device.

In some embodiments, the query includes an indication of the at leastsome of the nodes and the at least some of the entities to traverse toidentify the information.

In some embodiments, the information is at least some of the data valuesingested into the space graph.

In some embodiments, the instructions cause the one or more processorsto receive a request input from a user device in a natural language,generate, by a chatbot system, the query by performing natural languageprocessing on the request input, and provide the information to the userdevice.

Another implementation of the present disclosure is a method foroperating a building, the method including receiving, by a processingcircuit, building data from one or more building data sources,generating, by the processing circuit, a space graph based on thebuilding data, wherein the space graph is a graph data structureincluding nodes representing entities and a plurality edges between theentities representing relationships between the entities, ingesting, bythe processing circuit, data values of the building data into the spacegraph, the data values associated with the entities, and performing, bythe processing circuit, one or more operations with the space graphbased on both the relationships of the entities and the ingested datavalues.

In some embodiments, the entities include one or more agents, whereinthe agents comprise artificial intelligence. In some embodiments, themethod includes generating, by the processing circuit via the one ormore agents, one or more control decisions by querying the space graphfor information, wherein the information includes at least some of theentities, at least some of the relationships, and at least some of theingested data values and causing, by the processing circuit, one or morepieces of equipment to operate based on the one or more controldecisions.

In some embodiments, the space graph is a digital twin of the building,wherein the entities represent at least one of spaces, people, ordevices associated with the building.

In some embodiments, the instructions cause the one or more processorsto receiving, by the processing circuit, a query for information of thespace graph from a requesting device, wherein the information isincluded by one of the nodes of the space graph, retrieving, by theprocessing circuit, the information from the space graph by traversingat least some of the entities and at least some of the edges to identifythe information without traversing other entities or other relationshipsof a data structure other than the space graph, and providing, by theprocessing circuit, the information to the requesting device.

In some embodiments, the query includes an indication of the at leastsome of the nodes and the at least some of the entities to traverse toidentify the information.

In some embodiments, the information is at least some of the data valuesingested into the space graph.

In some embodiments, the instructions cause the one or more processorsto receiving, by the processing circuit, a request input from a userdevice in a natural language, generating, by the processing circuit viaa chatbot system, the query by performing natural language processing onthe request input, and providing, by the processing circuit, theinformation to the user device.

Another implementation of the present disclosure is a buildingmanagement system for operating a building and managing buildinginformation including a processing circuit configured to receivebuilding data from one or more building data sources, generate a spacegraph based on the building data, wherein the space graph is a graphdata structure including nodes representing entities and a pluralityedges between the entities representing relationships between theentities, ingest data values of the building data into the space graph,the data values associated with the entities, generate one or morecontrol decisions by querying the space graph for information, whereinthe information includes at least some of the entities, at least some ofthe relationships, and at least some of the ingested data values, andcause one or more pieces of equipment to operate based on the one ormore control decisions.

In some embodiments, the processing circuit is configured to generatethe one or more control decisions via an agent of the space graph,wherein the agent is one node of the nodes.

In some embodiments, the space graph is a digital twin of the building,wherein the entities represent at least one of spaces, people, anddevices associated with the building.

In some embodiments, the instructions cause the one or more processorsto receive a query for information of the space graph from a requestingdevice, wherein the information is included by one of the nodes of thespace graph, retrieve the information from the space graph by traversingat least some of the entities and at least some of the edges to identifythe information without traversing other entities or other relationshipsof a data structure other than the space graph, and provide theinformation to the requesting device.

In some embodiments, the query includes an indication of the at leastsome of the nodes and the at least some of the entities to traverse toidentify the information.

In some embodiments, the information is at least some of the data valuesingested into the space graph.

Dynamic Space Graph with New Entity Relationship Updates

Another implementation of the present disclosure is a building systemfor operating a building and managing building information, the buildingsystem including one or more memory devices configured to storeinstructions thereon, the instructions causing one or more processors togenerate a space graph based on building data, wherein the space graphis a graph data structure including nodes representing entities, edgesbetween the nodes representing relationships between the entities, anddata values of the building data associated with the entities, receivenew building data from one or more building data sources, generate,based on the new building data, a new relationship between a firstentity of the entities and a second entity of the entities, and updatethe space graph with the new relationship by causing the space graph tostore a new edge between a first node of the nodes representing thefirst entity and a second node of the nodes representing the secondentity.

In some embodiments, the instructions cause the one or more processorsto receive a query for information of the space graph from a requestingdevice, wherein the information is included by one of the nodes of thespace graph, retrieve the information from the space graph by traversingat least some of the entities and at least some of the edges to identifythe information without traversing other entities or other relationshipsof a data structure other than the space graph, and provide theinformation to the requesting device.

In some embodiments, the query includes an indication of the at leastsome of the nodes and the at least some of the entities to traverse toidentify the information.

In some embodiments, the instructions cause the one or more processorsto identify, based on the new building data, one or more new entitiesand update the space graph with the one or more new entities by causingthe space graph to store one or more new nodes.

In some embodiments, the instructions cause the one or more processorsto identify, based on the new building data, one or more additional newrelationships between the one or more new entities and update the spacegraph to store one or more additional new edges between the one or morenew nodes.

In some embodiments, the instructions cause the one or more processorsto identify, based on the new building data, one or more additional newrelationships between the one or more new entities and the entities andupdate the space graph to store one or more additional new edges betweenthe one or more new nodes and the nodes.

In some embodiments, the instructions cause the one or more processorsto generate, based on the new building data, the new relationshipbetween the first entity of the entities and the second entity of theentities by determining whether events are triggered by analyzing ruleswith the new building data, wherein each of the events is associatedwith one of the rules and determining, based on a pattern of the eventsthat are triggered, the new relationship.

In some embodiments, determining, based on the pattern of the eventsthat are triggered, the new relationship includes determining whether anumber of the events that are triggered is greater than a predefinedamount.

In some embodiments, each of the rules is a conditional rule based onwhether operational data of the entities exists and that at least someof the relationships exist, wherein the new building data is theoperational data.

Another implementation of the present disclosure is a method for abuilding system of a building. The method includes generating, by aprocessing circuit, a space graph based on building data, wherein thespace graph is a graph data structure including nodes representingentities, edges between the nodes representing relationships between theentities, and data values of the building data associated with theentities, receiving, by the processing circuit, new building data fromone or more building data sources, generating, by the processingcircuit, based on the new building data, a new relationship between afirst entity of the entities and a second entity of the entities, andupdating, by the processing circuit, the space graph with the newrelationship by causing the space graph to store a new edge between afirst node of the nodes representing the first entity and a second nodeof the nodes representing the second entity.

In some embodiments, the method includes receiving, by the processingcircuit, a query for information of the space graph from a requestingdevice, wherein the information is included by one of the nodes of thespace graph, retrieving, by the processing circuit, the information fromthe space graph by traversing at least some of the entities and at leastsome of the edges to identify the information without traversing otherentities or other relationships of a data structure other than the spacegraph, and providing, by the processing circuit, the information to therequesting device.

In some embodiments, the query includes an indication of the at leastsome of the nodes and the at least some of the entities to traverse toidentify the information.

In some embodiments, the method includes identifying, by the processingcircuit, based on the new building data, one or more new entities andupdating, by the processing circuit, the space graph with the one ormore new entities by causing the space graph to store one or more newnodes.

In some embodiments, the method includes identifying, by the processingcircuit, based on the new building data, one or more additional newrelationships between the one or more new entities and updating, by theprocessing circuit, the space graph to store one or more additional newedges between the one or more new nodes.

In some embodiments, the method includes identifying, by the processingcircuit, based on the new building data, one or more additional newrelationships between the one or more new entities and the entities andupdating, by the processing circuit, the space graph to store one ormore additional new edges between the one or more new nodes and thenodes.

In some embodiments, generating, by the processing circuit, based on thenew building data, the new relationship between the first entity of theentities and the second entity of the entities includes determiningwhether events are triggered by analyzing rules with the new buildingdata, wherein each of the events is associated with one of the rules anddetermining, based on a pattern of the events that are triggered, thenew relationship.

In some embodiments, determining, based on the pattern of the eventsthat are triggered, the new relationship includes determining whether anumber of the events that are triggered is greater than a predefinedamount.

In some embodiments, each of the rules is a conditional rule based onwhether operational data of the entities exists and that at least someof the relationships exist, wherein the new building data is theoperational data.

Another implementation of the present disclosure is a buildingmanagement system for operating a building, the building managementsystem including a processing circuit configured to generate a spacegraph based on building data, wherein the space graph is a graph datastructure including nodes representing entities, edges between the nodesrepresenting relationships between the entities, and data values of thebuilding data associated with the entities, receive new building datafrom one or more building data sources, generate, based on the newbuilding data, a new relationship between a first entity of the entitiesand a second entity of the entities, and update the space graph with thenew relationship by causing the space graph to store a new edge betweena first node of the nodes representing the first entity and a secondnode of the nodes representing the second entity.

In some embodiments, the processing circuit is configured to generate,based on the new building data, the new relationship between the firstentity of the entities and the second entity of the entities includesdetermining whether events are triggered by analyzing rules with the newbuilding data, wherein each of the events is associated with one of therules and determining, based on a pattern of the events that aretriggered, the new relationship.

Dynamic Building Control Based on Dynamic Space Graph

Another implementation of the present disclosure is a building systemfor operating a building and managing building information, the buildingsystem including one or more memory devices configured to storeinstructions thereon, the instructions causing one or more processors togenerate a space graph based on building data, wherein the space graphis a graph data structure including node representing entities, edgesbetween the nodes representing relationships between the entities, anddata values of the building data associated with the entities, generatea control algorithm based on the space graph and operate one or morepieces of building equipment based on the control algorithm, receive newbuilding data from one or more building data sources, generate one ormore new relationships between a first entity of the entities and asecond entity of the entities, update the space graph with the newrelationship by causing the space graph to store a new edge between afirst node of the nodes representing the first entity and a second nodeof the nodes representing the second entity, and update the controlalgorithm based on the new edge of the updated space graph and operatethe one or more pieces of building equipment based on the updatedcontrol algorithm.

In some embodiments, the entities include an agent, wherein theprocessing circuit is configured to update the control algorithm via theagent by querying, via the agent, the space graph for information,wherein the information includes at least some of the entities, at leastsome of the relationships, and the new edge.

In some embodiments, querying, via the agent, is performed at apredefined time interval.

In some embodiments, the instructions cause the one or more processorsto receive a query for information of the space graph from a requestingdevice, wherein the information is included by one of the nodes of thespace graph, retrieve the information from the space graph by traversingat least some of the entities and at least some of the edges to identifythe information without traversing other entities or other relationshipsof a data structure other than the space graph, and provide theinformation to the requesting device.

In some embodiments, the query includes an indication of the at leastsome of the nodes and the at least some of the entities to traverse toidentify the information.

In some embodiments, the instructions cause the one or more processorsto identify, based on the new building data, one or more new entitiesand update the space graph with the one or more new entities by causingthe space graph to store one or more new nodes.

In some embodiments, the instructions cause the one or more processorsto update the control algorithm based on the new edge of the updatedspace graph further based on the one or more new entities.

In some embodiments, the instructions cause the one or more processorsto generate, based on the new building data, the new relationshipbetween the first entity of the entities and the second entity of theentities by determining whether events are triggered by analyzing ruleswith the new building data, wherein each of the events is associatedwith one of the rules and determining, based on a pattern of the eventsthat are triggered, the new relationship.

In some embodiments, determining, based on the pattern of the eventsthat are triggered, the new relationship includes determining whether anumber of the events that are triggered is greater than a predefinedamount.

In some embodiments, each of the rules is a conditional rule based onwhether operational data of the entities exists and that at least someof the relationships exist, wherein the new building data is theoperational data.

Another implementation of the present disclosure is a method for abuilding system of a building, the method including generating, by aprocessing circuit, a space graph based on building data, wherein thespace graph is a graph data structure including node representingentities, edges between the nodes representing relationships between theentities, and data values of the building data associated with theentities, generating, by the processing circuit, a control algorithmbased on the space graph and operate one or more pieces of buildingequipment based on the control algorithm, receiving, by the processingcircuit, new building data from one or more building data sources,generating, by the processing circuit, one or more new relationshipsbetween a first entity of the entities and a second entity of theentities, updating, by the processing circuit, the space graph with thenew relationship by causing the space graph to store a new edge betweena first node of the nodes representing the first entity and a secondnode of the nodes representing the second entity, and updating, by theprocessing circuit, the control algorithm based on the new edge of theupdated space graph and operating, by the processing circuit, the one ormore pieces of building equipment based on the updated controlalgorithm.

In some embodiments, the entities include an agent, wherein updating, bythe processing circuit, the control algorithm is performed via the agentby querying, via the agent, the space graph for information, wherein theinformation includes at least some of the entities, at least some of therelationships, and the new edge.

In some embodiments, querying, via the agent, is performed at apredefined time interval.

In some embodiments, the method includes receiving, by the processingcircuit, a query for information of the space graph from a requestingdevice, wherein the information is included by one of the nodes of thespace graph, retrieving, by the processing circuit, the information fromthe space graph by traversing at least some of the entities and at leastsome of the edges to identify the information without traversing otherentities or other relationships of a data structure other than the spacegraph, and providing, by the processing circuit, the information to therequesting device.

In some embodiments, the query includes an indication of the at leastsome of the nodes and the at least some of the entities to traverse toidentify the information.

In some embodiments the method includes identifying, by the processingcircuit, based on the new building data, one or more new entities andupdating, by the processing circuit, the space graph with the one ormore new entities by causing the space graph to store one or more newnodes.

In some embodiments, updating, by the processing circuit the controlalgorithm based on the new edge of the updated space graph further basedon the one or more new entities.

In some embodiments, generating, by the processing circuit, based on thenew building data, the new relationship between the first entity of theentities and the second entity of the entities by determining whetherevents are triggered by analyzing rules with the new building data,wherein each of the events is associated with one of the rules anddetermining, based on a pattern of the events that are triggered, thenew relationship.

In some embodiments, determining, based on the pattern of the eventsthat are triggered, the new relationship includes determining whether anumber of the events that are triggered is greater than a predefinedamount.

Another implementation of the present disclosure is a buildingmanagement system for a building including a processing circuitconfigured to generate a space graph based on building data, wherein thespace graph is a graph data structure including node representingentities, edges between the nodes representing relationships between theentities, and data values of the building data associated with theentities, generate a control algorithm based on the space graph andoperate one or more pieces of building equipment based on the controlalgorithm, receive new building data from one or more building datasources, generate one or more new relationships between a first entityof the entities and a second entity of the entities, update the spacegraph with the new relationship by causing the space graph to store anew edge between a first node of the nodes representing the first entityand a second node of the nodes representing the second entity, andupdate the control algorithm based on the new edge of the updated spacegraph and operate the one or more pieces of building equipment based onthe updated control algorithm.

Dynamic Space Graph with Temporary Relationships

Another implementation of the present disclosure is a building systemfor operating a building and managing building information, the buildingsystem including one or more memory devices configured to storeinstructions thereon, the instructions causing one or more processors toreceive building data from one or more building data sources, generate atemporary relationship between a first entity and a second entity of aspace graph, wherein the space graph is a graph data structure includingnodes representing entities, edges between the nodes representingrelationships between the entities, and data values of the building dataassociated with the entities, cause the space graph to include thetemporary relationship by storing a temporary edge between a first nodeof the nodes representing the first entity and a second node of thenodes representing the second entity, perform one or more controloperations based on the space graph including the temporary edge,receive new building data from the one or more building data sources,determine whether to generate a permanent relationship to replace thetemporary relationship based on the new building data, and update thespace graph by causing the permanent relationship to replace thetemporary relationship of the space graph in response to a determinationto generate the permanent relationship to replace the temporaryrelationship by causing a permanent edge to replace the temporary edge.

In some embodiments, the instructions cause the one or more processorsto determine whether to generate a permanent relationship to replace thetemporary relationship include determining a confidence level for thepermanent relationship and replacing the temporary relationship with thepermanent relationship in response to a determination that theconfidence level is greater than a predefined amount.

In some embodiments, the temporary relationship is a singlerelationship, wherein the permanent relationship includes a firstrelationship between the first entity and the second entity and a secondrelationship between the second entity and the first entity.

In some embodiments, the instructions cause the one or more processorsto receive a query for information of the space graph from a requestingdevice, wherein the information is included by one of the nodes of thespace graph, retrieve the information from the space graph by traversingat least some of the entities and at least some of the edges to identifythe information without traversing other entities or other relationshipsof a data structure other than the space graph, and provide theinformation to the requesting device.

In some embodiments, the query includes an indication of the at leastsome of the nodes and the at least some of the entities to traverse toidentify the information.

In some embodiments, the instructions cause the one or more processorsto generate, based on the new building data, the temporary relationshipbetween the first entity of the entities and the second entity of theentities by determining whether events are triggered by analyzing ruleswith the new building data, wherein each of the events is associatedwith one of the rules and determining, based on a pattern of the eventsthat are triggered, the new relationship.

In some embodiments, determining, based on the pattern of the eventsthat are triggered, the new relationship includes determining whether anumber of the events that are triggered is greater than a predefinedamount.

In some embodiments, each of the rules is a conditional rule based onwhether operational data of the entities exists and that at least someof the relationships exist, wherein the new building data is theoperational data.

Another implementation of the present disclosure is a method for abuilding system of a building, the method including receiving, by aprocessing circuit, building data from one or more building datasources, generating, by the processing circuit, a temporary relationshipbetween a first entity and a second entity of a space graph, wherein thespace graph is a graph data structure including nodes representingentities, edges between the nodes representing relationships between theentities, and data values of the building data associated with theentities, causing, by the processing circuit, the space graph to includethe temporary relationship by storing a temporary edge between a firstnode of the nodes representing the first entity and a second node of thenodes representing the second entity, performing, by the processingcircuit, one or more control operations based on the space graphincluding the temporary edge, receiving, by the processing circuit, newbuilding data from the one or more building data sources, determining,by the processing circuit, whether to generate a permanent relationshipto replace the temporary relationship based on the new building data,and updating, by the processing circuit the space graph by causing thepermanent relationship to replace the temporary relationship of thespace graph in response to a determination to generate the permanentrelationship to replace the temporary relationship by causing apermanent edge to replace the temporary edge.

In some embodiments, determining, by the processing circuit, whether togenerate a permanent relationship to replace the temporary relationshipincludes determining a confidence level for the permanent relationshipand replacing the temporary relationship with the permanent relationshipin response to a determination that the confidence level is greater thana predefined amount.

In some embodiments, the temporary relationship is a singlerelationship, wherein the permanent relationship includes a firstrelationship between the first entity and the second entity and a secondrelationship between the second entity and the first entity.

In some embodiments, the method includes receiving, by the processingcircuit, a query for information of the space graph from a requestingdevice, wherein the information is included by one of the nodes of thespace graph, retrieving, by the processing circuit, the information fromthe space graph by traversing at least some of the entities and at leastsome of the edges to identify the information without traversing otherentities or other relationships of a data structure other than the spacegraph, and providing, by the processing circuit, the information to therequesting device.

In some embodiments, the query includes an indication of the at leastsome of the nodes and the at least some of the entities to traverse toidentify the information.

In some embodiments, generating, by the processing circuit, based on thenew building data, the temporary relationship between the first entityof the entities and the second entity of the entities includesdetermining whether events are triggered by analyzing rules with the newbuilding data, wherein each of the events is associated with one of therules and determining, based on a pattern of the events that aretriggered, the new relationship.

In some embodiments, determining, based on the pattern of the eventsthat are triggered, the new relationship includes determining whether anumber of the events that are triggered is greater than a predefinedamount.

In some embodiments, each of the rules is a conditional rule based onwhether operational data of the entities exists and that at least someof the relationships exist, wherein the new building data is theoperational data.

Another implementation of the present disclosure is a buildingmanagement system for operating a building and managing buildinginformation, the building management system including a processingcircuit configured to receive building data from one or more buildingdata sources, generate a temporary relationship between a first entityand a second entity of a space graph, wherein the space graph is a graphdata structure including nodes representing entities, edges between thenodes representing relationships between the entities, and data valuesof the building data associated with the entities, cause the space graphto include the temporary relationship by storing a temporary edgebetween a first node of the nodes representing the first entity and asecond node of the nodes representing the second entity, perform one ormore control operations based on the space graph including the temporaryedge, receive new building data from the one or more building datasources, determine whether to generate a permanent relationship toreplace the temporary relationship based on the new building data, andupdate the space graph by causing the permanent relationship to replacethe temporary relationship of the space graph in response to adetermination to generate the permanent relationship to replace thetemporary relationship by causing a permanent edge to replace thetemporary edge.

In some embodiments, the processing circuit is configured to determinewhether to generate a permanent relationship to replace the temporaryrelationship includes determining a confidence level for the permanentrelationship and replacing the temporary relationship with the permanentrelationship in response to a determination that the confidence level isgreater than a predefined amount.

In some embodiments, the temporary relationship is a singlerelationship, wherein the permanent relationship includes a firstrelationship between the first entity and the second entity and a secondrelationship between the second entity and the first entity.

In some embodiments, the processing circuit is configured to receive aquery for information of the space graph from a requesting device,wherein the information is included by one of the nodes of the spacegraph, retrieve the information from the space graph by traversing atleast some of the entities and at least some of the edges to identifythe information without traversing other entities or other relationshipsof a data structure other than the space graph, and provide theinformation to the requesting device.

Dynamic Space Graph with Indirect Impact Relationships

Another implementation of the present disclosure is a building systemfor operating a building and managing building information, the buildingsystem including one or more memory devices configured to storeinstructions thereon, the instructions causing one or more processors toreceive building data from one or more building data sources, identify,based on the building data, an indirect relationship between a firstentity and a second entity of a space graph, the indirect relationshipcaused by a control algorithm of the space graph, wherein the spacegraph is a graph data structure including nodes representing entities,edges between the nodes representing relationships between the entities,and data values of the building data associated with the entities,update the space graph with the indirect relationship by causing thespace graph to include an indirect relationship edge between a firstnode of the nodes representing the first entity and a second node of thenodes representing the second entity, update the control algorithm ofthe space graph based on the indirect relationship edge, and operate oneor more pieces of building equipment based on the updated controlalgorithm.

In some embodiments, one of the entities is an agent configured toperform artificial intelligence to update the control algorithm of thespace graph based on the indirect relationship edge.

In some embodiments, the building data environmental condition dataindicating an environmental condition of the second entity, wherein theinstructions cause the one or more processors to identify the indirectrelationship between the first entity and the second entity based onchanges in the environmental condition and control commands associatedof the first entity.

In some embodiments, the indirect relationship indicates that performingone or more operations by the first entity indirectly affects anenvironmental condition of the second entity.

In some embodiments, wherein the entities further includes a first zone,wherein the second entity is a second zone, wherein the first entity isa piece of building equipment configured to control an environmentalcondition of the first zone, wherein the piece of building equipmentcontrolling the environmental condition of the first zone indirectlyaffects the environmental condition of the second zone.

In some embodiments, the instructions cause the one or more processorsto receive a query for information of the space graph from a requestingdevice, wherein the information is included by one of the nodes of thespace graph, retrieve the information from the space graph by traversingat least some of the entities and at least some of the edges to identifythe information without traversing other entities or other relationshipsof a data structure other than the space graph, and provide theinformation to the requesting device.

In some embodiments, the query includes an indication of the at leastsome of the nodes and the at least some of the entities to traverse toidentify the information.

Another implementation of the present disclosure is a method a buildingsystem of a building, the method includes receiving, by a processingcircuit, building data from one or more building data sources,identifying, by the processing circuit, based on the building data, anindirect relationship between a first entity and a second entity of aspace graph, the indirect relationship caused by a control algorithm ofthe space graph, wherein the space graph is a graph data structureincluding nodes representing entities, edges between the nodesrepresenting relationships between the entities, and data values of thebuilding data associated with the entities, updating, by the processingcircuit, the space graph with the indirect relationship by causing thespace graph to include an indirect relationship edge between a firstnode of the nodes representing the first entity and a second node of thenodes representing the second entity, updating, by the processingcircuit, the control algorithm of the space graph based on the indirectrelationship edge, and operating, by the processing circuit, one or morepieces of building equipment based on the updated control algorithm.

In some embodiments, one of the entities is an agent configured toperform artificial intelligence to update the control algorithm of thespace graph based on the indirect relationship edge.

In some embodiments, the building data includes environmental conditiondata indicating an environmental condition of the second entity, whereinthe instructions cause the one or more processors to identify theindirect relationship between the first entity and the second entitybased on changes in the environmental condition and control commandsassociated of the first entity.

In some embodiments, the indirect relationship indicates that performingone or more operations by the first entity indirectly affects anenvironmental condition of the second entity.

In some embodiments, the entities further includes a first zone, whereinthe second entity is a second zone, wherein the first entity is a pieceof building equipment configured to control an environmental conditionof the first zone, wherein the piece of building equipment controllingthe environmental condition of the first zone indirectly affects theenvironmental condition of the second zone.

In some embodiments, the method includes receiving, by the processingcircuit, a query for information of the space graph from a requestingdevice, wherein the information is included by one of the nodes of thespace graph, retrieving, by the processing circuit, the information fromthe space graph by traversing at least some of the entities and at leastsome of the edges to identify the information without traversing otherentities or other relationships of a data structure other than the spacegraph, and providing, by the processing circuit, the information to therequesting device.

In some embodiments, the query includes an indication of the at leastsome of the nodes and the at least some of the entities to traverse toidentify the information.

Another implementation of the present disclosure is a buildingmanagement system for operating a building and managing buildinginformation, the building management system including a processingcircuit configured to receive building data from one or more buildingdata sources, identify, based on the building data, an indirectrelationship between a first entity and a second entity of a spacegraph, the indirect relationship caused by a control algorithm of thespace graph, wherein the space graph is a graph data structure includingnodes representing entities, edges between the nodes representingrelationships between the entities, and data values of the building dataassociated with the entities, update the space graph with the indirectrelationship by causing the space graph to include an indirectrelationship edge between a first node of the nodes representing thefirst entity and a second node of the nodes representing the secondentity, update the control algorithm of the space graph based on theindirect relationship edge, and operate one or more pieces of buildingequipment based on the updated control algorithm.

In some embodiments, one of the entities is an agent configured toperform artificial intelligence to update the control algorithm of thespace graph based on the indirect relationship edge.

In some embodiments, the building data includes environmental conditiondata indicating an environmental condition of the second entity, whereinthe instructions cause the one or more processors to identify theindirect relationship between the first entity and the second entitybased on changes in the environmental condition and control commandsassociated of the first entity.

In some embodiments, the indirect relationship indicates that performingone or more operations by the first entity indirectly affects anenvironmental condition of the second entity.

In some embodiments, the entities further includes a first zone, whereinthe second entity is a second zone, wherein the first entity is a pieceof building equipment configured to control an environmental conditionof the first zone, wherein the piece of building equipment controllingthe environmental condition of the first zone indirectly affects theenvironmental condition of the second zone.

In some embodiments, the instructions cause the one or more processorsto receive a query for information of the space graph from a requestingdevice, wherein the information is included by one of the nodes of thespace graph, retrieve the information from the space graph by traversingat least some of the entities and at least some of the edges to identifythe information without traversing other entities or other relationshipsof a data structure other than the space graph, and provide theinformation to the requesting device.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects and features of the present disclosure willbecome more apparent to those skilled in the art from the followingdetailed description of the example embodiments with reference to theaccompanying drawings.

FIG. 1 is a block diagram of a smart building environment, according toan exemplary embodiment.

FIG. 2 is a perspective view of a smart building, according to anexemplary embodiment.

FIG. 3 is a block diagram of a waterside system, according to anexemplary embodiment.

FIG. 4 is a block diagram of an airside system, according to anexemplary embodiment.

FIG. 5 is a block diagram of a building management system, according toan exemplary embodiment.

FIG. 6 is a block diagram of another building management systemincluding a timeseries service and an entity service, according to anexemplary embodiment.

FIG. 7 is a block diagram illustrating the entity service of FIG. 6 ingreater detail, according to an exemplary embodiment

FIG. 8 in an example entity graph of entity data, according to anexemplary embodiment.

FIG. 9 is a block diagram illustrating the timeseries service of FIG. 6in greater detail, according to an exemplary embodiment.

FIG. 10 is an example entity graph of entity data, according to anexemplary embodiment.

FIG. 11 is a block diagram of the entity service of FIG. 6 managing aspace graph, according to an exemplary embodiment.

FIG. 12 is a block diagram of applications for a building utilizing thespace graph of FIG. 11, according to an exemplary embodiment.

FIG. 13 is a block diagram of the space graph of FIG. 11 in greaterdetail, according to an exemplary embodiment.

FIG. 14 is a block diagram of the space graph of FIG. 13 where a newrelationship for the space graph is learned and a temporary relationshipis added, according to an exemplary embodiment.

FIG. 15 is a block diagram of the space graph of FIG. 14 where apermanent relationship is learned for replacing the temporaryrelationship, according to an exemplary embodiment.

FIG. 16 is a block diagram of the space graph of FIG. 15 where an impactrelationship is learned indicating an effect of a control algorithm onmultiple entities, according to an exemplary embodiment.

FIG. 17 is a flow diagram of a process for generating the space graph ofFIG. 11 wherein a first entity and a second entity include a firstrelationship between the first entity and the second entity and a secondrelationship between the second entity and the first entity, accordingto an exemplary embodiment.

FIG. 18 is a flow diagram of a process for learning new relationshipsfor the space graph of FIG. 11 and updating the space graph with the newrelationships, according to an exemplary embodiment.

FIG. 19 is a flow diagram of a process for learning new relationshipsfor the space graph of FIG. 11, updating the space graph with the newrelationships, and updating control algorithms based on the updatedspace graph, according to an exemplary embodiment.

FIG. 20 is a flow diagram of a process of learning temporaryrelationships for the space graph of FIG. 11 and replacing the temporaryrelationships with permanent relationships, according to an exemplaryembodiment.

FIG. 21 is a flow diagram of a process of learning an impactrelationship between entities of the space graph of FIG. 11, the impactrelationship indicating the effects of a control algorithm performed forone entity on another entity, according to an exemplary embodiment.

DETAILED DESCRIPTION

Referring generally to the FIGURES, a building management system withspace graphs is shown, according to various exemplary embodiments. Thespace graph can be a data structure allowing entities, relationships,and/or information to be stored for operating a building. The spacegraph can be a data structure including nodes and edges, the nodesrepresenting particular entities and the edges between the nodesrepresenting a relationship between the entities. Any number of nodesand edges can exist in the space graph along with ingested data for eachnode, e.g., collected data associated with an entity represented by thenode. In this regard, the space graph can be a total representation, adigital twin, of an entire space since the space graph can represent theentities of the space, relationships between the entities, and data forthe entities.

In some embodiments, there may be multiple relationships betweenentities of the space graph. For example, a first entity of a spacegraph can be linked to a second entity of a space graph with a firstrelationship while the second entity can be linked to the first entitywith a different relationship. For example, a variable air volume and athermostat could be related to each other, however, their relationshipsto each other are different. The thermostat can be configured to controlthe VAV while the VAV can be configured to receive commands from thethermostat. Both relationships, though different, can be captured in thespace graph.

In some embodiments, the space graph includes agents. The agents can beparticular nodes of the space graph. The agents can be artificialintelligence components configured to receive inputs, e.g., otherinformation from the space graph, and/or generate changes or updates tothe space graph based on the information. For example, a space graphcould be a comfort agent configured to generate control schedules for athermostat based on information of the space graph. In this regard, thecomfort agent can be configured to receive information from the spacegraph relating to the thermostat, spaces controlled by the thermostat,and activities within the spaces, to generate a control schedule. Thecontrol schedule can be an entity of the space graph which implementsoperation of the thermostat and/or may be associated with a relationshipbetween the agent and the control schedule and a relationship betweenthe control schedule and the thermostat.

Furthermore, the space graph can be a dynamic data structure that adaptsbased on information collected from the space represented by the spacegraph and/or ingested into the space graph. For example, the buildingsystem can be configured to update the space graph to include newrelationships and/or entities overtime such that as changes are made tothe space represented by the space graph, the space graph adapts to thechanges. In some cases, the building system is configured to addtemporary relationships to the space graph. The temporary relationshipsmay indicate a potential relationship between entities of the spacegraph.

Overtime, the building system can determine whether the temporaryrelationship should exist permanently in the space graph and caneventually replace the temporary relationship with a permanentrelationship. Furthermore, the temporary relationship may not include adescription of the relationship type but may be a placeholder indicatingthat some specific type of relationship exists between two entities. Thepermanent relationship that replaces the temporary relationship mayinclude a description of the relationship derived from information ofthe space graph. For example, if the system adds a temporaryrelationship between a thermostat and a space, the temporaryrelationship indicating that the thermostat and the space areassociated, the permanent relationship may indicate how the thermostatand the space are relation, e.g., that the thermostat controlstemperature of the space.

Furthermore, the various agents can periodically scan the space graphovertime to update the control configurations they generate. Forexample, as new entities and/or new relationships are added to the spacegraphs, the agents can update their control configurations to accountfor relationships that are added or removed from the space graph,dynamically updating their control.

In some embodiments, the building management system can add impactrelationships between entities of the space graph. The result of aparticular control configuration generated by an agent for the spacegraph may have an indirect impact on another entity. The buildingmanagement system can detect the impact relationship and add therelationship to the space graph to represent the impact which thecontrol configuration has on the entity. For example, heating or coolinga particular zone may have an impact on the temperature of a neighboringzone. Therefore, a direct relationship may exist between heating orcooling equipment and the first zone while an impact relationship mayexist between the heating or cooling equipment and the neighboring zone.

This application is related to U.S. patent application Ser. No.15/723,624 filed Oct. 3, 2017, U.S. Provisional Patent Application No.62/611,974 filed Dec. 29, 2017, U.S. Provisional Patent Application No.62/612,228 filed Dec. 29, 2017, U.S. Provisional Patent Application No.62/611,962 filed Dec. 29, 2017, U.S. Provisional Patent Application No.62/627,615 filed Feb. 7, 2018, U.S. patent application Ser. No.15/934,593 filed Mar. 23, 2018, U.S. patent application Ser. No.16/008,885 filed Jun. 14, 2018, and U.S. patent application Ser. No.16/036,685 filed Jul. 16, 2018. The entirety of each of these patentapplications is incorporated by reference herein.

Hereinafter, example embodiments will be described in more detail withreference to the accompanying drawings. FIG. 1 is a block diagram of asmart building environment 100, according to some exemplary embodiments.Smart building environment 100 is shown to include a building managementplatform 102. Building management platform 102 can be configured tocollect data from a variety of different data sources. For example,building management platform 102 is shown collecting data from buildings110, 120, 130, and 140. For example, the buildings may include a school110, a hospital 120, a factory 130, an office building 140, and/or thelike. However, the present disclosure is not limited to the number ortypes of buildings 110, 120, 130, and 140 shown in FIG. 1. For example,in some embodiments, building management platform 102 may be configuredto collect data from one or more buildings, and the one or morebuildings may be the same type of building, or may include one or moredifferent types of buildings than that shown in FIG. 1.

Building management platform 102 can be configured to collect data froma variety of devices 112-116, 122-126, 132-136, and 142-146, eitherdirectly (e.g., directly via network 104) or indirectly (e.g., viasystems or applications in the buildings 110, 120, 130, 140). In someembodiments, devices 112-116, 122-126, 132-136, and 142-146 are internetof things (IoT) devices. IoT devices may include any of a variety ofphysical devices, sensors, actuators, electronics, vehicles, homeappliances, and/or other items having network connectivity which enableIoT devices to communicate with building management platform 102. Forexample, IoT devices can include smart home hub devices, smart housedevices, doorbell cameras, air quality sensors, smart switches, smartlights, smart appliances, garage door openers, smoke detectors, heartmonitoring implants, biochip transponders, cameras streaming live feeds,automobiles with built-in sensors, DNA analysis devices, field operationdevices, tracking devices for people/vehicles/equipment, networkedsensors, wireless sensors, wearable sensors, environmental sensors, RFIDgateways and readers, IoT gateway devices, robots and other roboticdevices, GPS devices, smart watches, virtual/augmented reality devices,and/or other networked or networkable devices. While the devicesdescribed herein are generally referred to as IoT devices, it should beunderstood that, in various embodiments, the devices referenced in thepresent disclosure could be any type of devices capable of communicatingdata over an electronic network.

In some embodiments, IoT devices may include sensors or sensor systems.For example, IoT devices may include acoustic sensors, sound sensors,vibration sensors, automotive or transportation sensors, chemicalsensors, electric current sensors, electric voltage sensors, magneticsensors, radio sensors, environment sensors, weather sensors, moisturesensors, humidity sensors, flow sensors, fluid velocity sensors,ionizing radiation sensors, subatomic particle sensors, navigationinstruments, position sensors, angle sensors, displacement sensors,distance sensors, speed sensors, acceleration sensors, optical sensors,light sensors, imaging devices, photon sensors, pressure sensors, forcesensors, density sensors, level sensors, thermal sensors, heat sensors,temperature sensors, proximity sensors, presence sensors, and/or anyother type of sensors or sensing systems.

Examples of acoustic, sound, or vibration sensors include geophones,hydrophones, lace sensors, guitar pickups, microphones, andseismometers. Examples of automotive or transportation sensors includeair flow meters, air-fuel ratio (AFR) meters, blind spot monitors,crankshaft position sensors, defect detectors, engine coolanttemperature sensors, Hall effect sensors, knock sensors, map sensors,mass flow sensors, oxygen sensors, parking sensors, radar guns,speedometers, speed sensors, throttle position sensors, tire-pressuremonitoring sensors, torque sensors, transmission fluid temperaturesensors, turbine speed sensors, variable reluctance sensors, vehiclespeed sensors, water sensors, and wheel speed sensors.

Examples of chemical sensors include breathalyzers, carbon dioxidesensors, carbon monoxide detectors, catalytic bead sensors, chemicalfield-effect transistors, chemiresistors, electrochemical gas sensors,electronic noses, electrolyte-insulator-semiconductor sensors,fluorescent chloride sensors, holographic sensors, hydrocarbon dew pointanalyzers, hydrogen sensors, hydrogen sulfide sensors, infrared pointsensors, ion-selective electrodes, nondispersive infrared sensors,microwave chemistry sensors, nitrogen oxide sensors, olfactometers,optodes, oxygen sensors, ozone monitors, pellistors, pH glasselectrodes, potentiometric sensors, redox electrodes, smoke detectors,and zinc oxide nanorod sensors.

Examples of electromagnetic sensors include current sensors, Dalydetectors, electroscopes, electron multipliers, Faraday cups,galvanometers, Hall effect sensors, Hall probes, magnetic anomalydetectors, magnetometers, magnetoresistances, mems magnetic fieldsensors, metal detectors, planar hall sensors, radio direction finders,and voltage detectors.

Examples of environmental sensors include actinometers, air pollutionsensors, bedwetting alarms, ceilometers, dew warnings, electrochemicalgas sensors, fish counters, frequency domain sensors, gas detectors,hook gauge evaporimeters, humistors, hygrometers, leaf sensors,lysimeters, pyranometers, pyrgeometers, psychrometers, rain gauges, rainsensors, seismometers, SNOTEL sensors, snow gauges, soil moisturesensors, stream gauges, and tide gauges. Examples of flow and fluidvelocity sensors include air flow meters, anemometers, flow sensors, gasmeter, mass flow sensors, and water meters.

Examples of radiation and particle sensors include cloud chambers,Geiger counters, Geiger-Muller tubes, ionisation chambers, neutrondetections, proportional counters, scintillation counters, semiconductordetectors, and thermoluminescent dosimeters. Examples of navigationinstruments include air speed indicators, altimeters, attitudeindicators, depth gauges, fluxgate compasses, gyroscopes, inertialnavigation systems, inertial reference nits, magnetic compasses, MHDsensors, ring laser gyroscopes, turn coordinators, tialinx sensors,variometers, vibrating structure gyroscopes, and yaw rate sensors.

Examples of position, angle, displacement, distance, speed, andacceleration sensors include auxanometers, capacitive displacementsensors, capacitive sensing devices, flex sensors, free fall sensors,gravimeters, gyroscopic sensors, impact sensors, inclinometers,integrated circuit piezoelectric sensors, laser rangefinders, lasersurface velocimeters, Light Detection And Ranging (LIDAR) sensors,linear encoders, linear variable differential transformers (LVDT),liquid capacitive inclinometers odometers, photoelectric sensors,piezoelectric accelerometers, position sensors, position sensitivedevices, angular rate sensors, rotary encoders, rotary variabledifferential transformers, selsyns, shock detectors, shock data loggers,tilt sensors, tachometers, ultrasonic thickness gauges, variablereluctance sensors, and velocity receivers.

Examples of optical, light, imaging, and photon sensors includecharge-coupled devices, complementary metal-oxide-semiconductor (CMOS)sensors, colorimeters, contact image sensors, electro-optical sensors,flame detectors, infra-red sensors, kinetic inductance detectors, led aslight sensors, light-addressable potentiometric sensors, Nicholsradiometers, fiber optic sensors, optical position sensors, thermopilelaser sensors, photodetectors, photodiodes, photomultiplier tubes,phototransistors, photoelectric sensors, photoionization detectors,photomultipliers, photoresistors, photoswitches, phototubes,scintillometers, Shack-Hartmann sensors, single-photon avalanche diodes,superconducting nanowire single-photon detectors, transition edgesensors, visible light photon counters, and wavefront sensors.

Examples of pressure sensors include barographs, barometers, boostgauges, bourdon gauges, hot filament ionization gauges, ionizationgauges, McLeod gauges, oscillating u-tubes, permanent downhole gauges,piezometers, pirani gauges, pressure sensors, pressure gauges, tactilesensors, and time pressure gauges. Examples of force, density, and levelsensors include bhangmeters, hydrometers, force gauge and force sensors,level sensors, load cells, magnetic level gauges, nuclear densitygauges, piezocapacitive pressure sensors, piezoelectric sensors, straingauges, torque sensors, and viscometers.

Examples of thermal, heat, and temperature sensors include bolometers,bimetallic strips, calorimeters, exhaust gas temperature gauges, flamedetections, Gardon gauges, Golay cells, heat flux sensors, infraredthermometers, microbolometers, microwave radiometers, net radiometers,quartz thermometers, resistance thermometers, silicon bandgaptemperature sensors, special sensor microwave/imagers, temperaturegauges, thermistors, thermocouples, thermometers, and pyrometers.Examples of proximity and presence sensors include alarm sensors,Doppler radars, motion detectors, occupancy sensors, proximity sensors,passive infrared sensors, reed switches, stud finders, triangulationsensors, touch switches, and wired gloves.

In some embodiments, different sensors send measurements or other datato building management platform 102 using a variety of differentcommunications protocols or data formats. Building management platform102 can be configured to ingest sensor data received in any protocol ordata format and translate the inbound sensor data into a common dataformat. Building management platform 102 can create a sensor objectsmart entity for each sensor that communicates with Building managementplatform 102. Each sensor object smart entity may include one or morestatic attributes that describe the corresponding sensor, one or moredynamic attributes that indicate the most recent values collected by thesensor, and/or one or more relational attributes that relate sensorsobject smart entities to each other and/or to other types of smartentities (e.g., space entities, system entities, data entities, etc.).

In some embodiments, building management platform 102 stores sensor datausing data entities. Each data entity may correspond to a particularsensor and may include a timeseries of data values received from thecorresponding sensor. In some embodiments, building management platform102 stores relational entities that define relationships between sensorobject entities and the corresponding data entity. For example, eachrelational entity may identify a particular sensor object entity, aparticular data entity, and may define a link between such entities.

Building management platform 102 can collect data from a variety ofexternal systems or services. For example, building management platform102 is shown receiving weather data from a weather service 152, newsdata from a news service 154, documents and other document-related datafrom a document service 156, and media (e.g., video, images, audio,social media, etc.) from a media service 158 (hereinafter referred tocollectively as 3^(rd) party services). In some embodiments, buildingmanagement platform 102 generates data internally. For example, buildingmanagement platform 102 may include a web advertising system, a websitetraffic monitoring system, a web sales system, or other types ofplatform services that generate data. The data generated by buildingmanagement platform 102 can be collected, stored, and processed alongwith the data received from other data sources. Building managementplatform 102 can collect data directly from external systems or devicesor via a network 104 (e.g., a WAN, the Internet, a cellular network,etc.). Building management platform 102 can process and transformcollected data to generate timeseries data and entity data. Severalfeatures of building management platform 102 are described in moredetail below.

Building HVAC Systems and Building Management Systems

Referring now to FIGS. 2-5, several building management systems (BMS)and HVAC systems in which the systems and methods of the presentdisclosure can be implemented are shown, according to some embodiments.In brief overview, FIG. 2 shows a building 10 equipped with, forexample, a HVAC system 200. Building 10 may be any of the buildings 210,220, 230, and 140 as shown in FIG. 1, or may be any other suitablebuilding that is communicatively connected to building managementplatform 102. FIG. 3 is a block diagram of a waterside system 300 whichcan be used to serve building 10. FIG. 4 is a block diagram of anairside system 400 which can be used to serve building 10. FIG. 5 is ablock diagram of a building management system (BMS) which can be used tomonitor and control building 10.

Building and HVAC System

Referring particularly to FIG. 2, a perspective view of a smart building10 is shown. Building 10 is served by a BMS. A BMS is, in general, asystem of devices configured to control, monitor, and manage equipmentin or around a building or building area. A BMS can include, forexample, a HVAC system, a security system, a lighting system, a firealerting system, and any other system that is capable of managingbuilding functions or devices, or any combination thereof. Further, eachof the systems may include sensors and other devices (e.g., IoT devices)for the proper operation, maintenance, monitoring, and the like of therespective systems.

The BMS that serves building 10 includes a HVAC system 200. HVAC system200 can include HVAC devices (e.g., heaters, chillers, air handlingunits, pumps, fans, thermal energy storage, etc.) configured to provideheating, cooling, ventilation, or other services for building 10. Forexample, HVAC system 200 is shown to include a waterside system 220 andan airside system 230. Waterside system 220 may provide a heated orchilled fluid to an air handling unit of airside system 230. Airsidesystem 230 may use the heated or chilled fluid to heat or cool anairflow provided to building 10. An exemplary waterside system andairside system which can be used in HVAC system 200 are described ingreater detail with reference to FIGS. 3 and 4.

HVAC system 200 is shown to include a chiller 202, a boiler 204, and arooftop air handling unit (AHU) 206. Waterside system 220 may use boiler204 and chiller 202 to heat or cool a working fluid (e.g., water,glycol, etc.) and may circulate the working fluid to AHU 206. In variousembodiments, the HVAC devices of waterside system 220 can be located inor around building 10 (as shown in FIG. 2) or at an offsite locationsuch as a central plant (e.g., a chiller plant, a steam plant, a heatplant, etc.). The working fluid can be heated in boiler 204 or cooled inchiller 202, depending on whether heating or cooling is required inbuilding 10. Boiler 204 may add heat to the circulated fluid, forexample, by burning a combustible material (e.g., natural gas) or usingan electric heating element. Chiller 202 may place the circulated fluidin a heat exchange relationship with another fluid (e.g., a refrigerant)in a heat exchanger (e.g., an evaporator) to absorb heat from thecirculated fluid. The working fluid from chiller 202 and/or boiler 204can be transported to AHU 206 via piping 208.

AHU 206 may place the working fluid in a heat exchange relationship withan airflow passing through AHU 206 (e.g., via one or more stages ofcooling coils and/or heating coils). The airflow can be, for example,outside air, return air from within building 10, or a combination ofboth. AHU 206 may transfer heat between the airflow and the workingfluid to provide heating or cooling for the airflow. For example, AHU206 can include one or more fans or blowers configured to pass theairflow over or through a heat exchanger containing the working fluid.The working fluid may then return to chiller 202 or boiler 204 viapiping 210.

Airside system 230 may deliver the airflow supplied by AHU 206 (i.e.,the supply airflow) to building 10 via air supply ducts 212 and mayprovide return air from building 10 to AHU 206 via air return ducts 214.In some embodiments, airside system 230 includes multiple variable airvolume (VAV) units 216. For example, airside system 230 is shown toinclude a separate VAV unit 216 on each floor or zone of building 10.VAV units 216 can include dampers or other flow control elements thatcan be operated to control an amount of the supply airflow provided toindividual zones of building 10. In other embodiments, airside system230 delivers the supply airflow into one or more zones of building 10(e.g., via supply ducts 212) without using intermediate VAV units 216 orother flow control elements. AHU 206 can include various sensors (e.g.,temperature sensors, pressure sensors, etc.) configured to measureattributes of the supply airflow. AHU 206 may receive input from sensorslocated within AHU 206 and/or within the building zone and may adjustthe flow rate, temperature, or other attributes of the supply airflowthrough AHU 206 to achieve setpoint conditions for the building zone.

Waterside System

Referring now to FIG. 3, a block diagram of a waterside system 300 isshown, according to some embodiments. In various embodiments, watersidesystem 300 may supplement or replace waterside system 220 in HVAC system200 or can be implemented separate from HVAC system 200. Whenimplemented in HVAC system 200, waterside system 300 can include asubset of the HVAC devices in HVAC system 200 (e.g., boiler 204, chiller202, pumps, valves, etc.) and may operate to supply a heated or chilledfluid to AHU 206. The HVAC devices of waterside system 300 can belocated within building 10 (e.g., as components of waterside system 220)or at an offsite location such as a central plant.

In FIG. 3, waterside system 300 is shown as a central plant havingsubplants 302-312. Subplants 302-312 are shown to include a heatersubplant 302, a heat recovery chiller subplant 304, a chiller subplant306, a cooling tower subplant 308, a hot thermal energy storage (TES)subplant 310, and a cold thermal energy storage (TES) subplant 312.Subplants 302-312 consume resources (e.g., water, natural gas,electricity, etc.) from utilities to serve thermal energy loads (e.g.,hot water, cold water, heating, cooling, etc.) of a building or campus.For example, heater subplant 302 can be configured to heat water in ahot water loop 314 that circulates the hot water between heater subplant302 and building 10. Chiller subplant 306 can be configured to chillwater in a cold water loop 316 that circulates the cold water betweenchiller subplant 306 and building 10. Heat recovery chiller subplant 304can be configured to transfer heat from cold water loop 316 to hot waterloop 314 to provide additional heating for the hot water and additionalcooling for the cold water. Condenser water loop 318 may absorb heatfrom the cold water in chiller subplant 306 and reject the absorbed heatin cooling tower subplant 308 or transfer the absorbed heat to hot waterloop 314. Hot TES subplant 310 and cold TES subplant 312 may store hotand cold thermal energy, respectively, for subsequent use.

Hot water loop 314 and cold water loop 316 may deliver the heated and/orchilled water to air handlers located on the rooftop of building 10(e.g., AHU 206) or to individual floors or zones of building 10 (e.g.,VAV units 216). The air handlers push air past heat exchangers (e.g.,heating coils or cooling coils) through which the water flows to provideheating or cooling for the air. The heated or cooled air can bedelivered to individual zones of building 10 to serve thermal energyloads of building 10. The water then returns to subplants 302-312 toreceive further heating or cooling.

Although subplants 302-312 are shown and described as heating andcooling water for circulation to a building, it is understood that anyother type of working fluid (e.g., glycol, CO2, etc.) can be used inplace of or in addition to water to serve thermal energy loads. In otherembodiments, subplants 302-312 may provide heating and/or coolingdirectly to the building or campus without requiring an intermediateheat transfer fluid. These and other variations to waterside system 300are within the teachings of the present disclosure.

Each of subplants 302-312 can include a variety of equipment configuredto facilitate the functions of the subplant. For example, heatersubplant 302 is shown to include heating elements 320 (e.g., boilers,electric heaters, etc.) configured to add heat to the hot water in hotwater loop 314. Heater subplant 302 is also shown to include severalpumps 322 and 324 configured to circulate the hot water in hot waterloop 314 and to control the flow rate of the hot water throughindividual heating elements 320. Chiller subplant 306 is shown toinclude chillers 332 configured to remove heat from the cold water incold water loop 316. Chiller subplant 306 is also shown to includeseveral pumps 334 and 336 configured to circulate the cold water in coldwater loop 316 and to control the flow rate of the cold water throughindividual chillers 332.

Heat recovery chiller subplant 304 is shown to include heat recoveryheat exchangers 326 (e.g., refrigeration circuits) configured totransfer heat from cold water loop 316 to hot water loop 314. Heatrecovery chiller subplant 304 is also shown to include several pumps 328and 330 configured to circulate the hot water and/or cold water throughheat recovery heat exchangers 326 and to control the flow rate of thewater through individual heat recovery heat exchangers 326. Coolingtower subplant 308 is shown to include cooling towers 338 configured toremove heat from the condenser water in condenser water loop 318.Cooling tower subplant 308 is also shown to include several pumps 340configured to circulate the condenser water in condenser water loop 318and to control the flow rate of the condenser water through individualcooling towers 338.

Hot TES subplant 310 is shown to include a hot TES tank 342 configuredto store the hot water for later use. Hot TES subplant 310 may alsoinclude one or more pumps or valves configured to control the flow rateof the hot water into or out of hot TES tank 342. Cold TES subplant 312is shown to include cold TES tanks 344 configured to store the coldwater for later use. Cold TES subplant 312 may also include one or morepumps or valves configured to control the flow rate of the cold waterinto or out of cold TES tanks 344.

In some embodiments, one or more of the pumps in waterside system 300(e.g., pumps 322, 324, 328, 330, 334, 336, and/or 340) or pipelines inwaterside system 300 include an isolation valve associated therewith.Isolation valves can be integrated with the pumps or positioned upstreamor downstream of the pumps to control the fluid flows in watersidesystem 300. In various embodiments, waterside system 300 can includemore, fewer, or different types of devices and/or subplants based on theparticular configuration of waterside system 300 and the types of loadsserved by waterside system 300.

Airside System

Referring now to FIG. 4, a block diagram of an airside system 400 isshown, according to some embodiments. In various embodiments, airsidesystem 400 may supplement or replace airside system 230 in HVAC system200 or can be implemented separate from HVAC system 200. Whenimplemented in HVAC system 200, airside system 400 can include a subsetof the HVAC devices in HVAC system 200 (e.g., AHU 206, VAV units 216,ducts 212-214, fans, dampers, etc.) and can be located in or aroundbuilding 10. Airside system 400 may operate to heat or cool an airflowprovided to building 10 using a heated or chilled fluid provided bywaterside system 300.

In FIG. 4, airside system 400 is shown to include an economizer-type airhandling unit (AHU) 402. Economizer-type AHUs vary the amount of outsideair and return air used by the air handling unit for heating or cooling.For example, AHU 402 may receive return air 404 from building zone 406via return air duct 408 and may deliver supply air 410 to building zone406 via supply air duct 412. In some embodiments, AHU 402 is a rooftopunit located on the roof of building 10 (e.g., AHU 206 as shown in FIG.2) or otherwise positioned to receive both return air 404 and outsideair 414. AHU 402 can be configured to operate exhaust air damper 416,mixing damper 418, and outside air damper 420 to control an amount ofoutside air 414 and return air 404 that combine to form supply air 410.Any return air 404 that does not pass through mixing damper 418 can beexhausted from AHU 402 through exhaust damper 416 as exhaust air 422.

Each of dampers 416-420 can be operated by an actuator. For example,exhaust air damper 416 can be operated by actuator 424, mixing damper418 can be operated by actuator 426, and outside air damper 420 can beoperated by actuator 428. Actuators 424-428 may communicate with an AHUcontroller 430 via a communications link 432. Actuators 424-428 mayreceive control signals from AHU controller 430 and may provide feedbacksignals to AHU controller 430. Feedback signals can include, forexample, an indication of a current actuator or damper position, anamount of torque or force exerted by the actuator, diagnosticinformation (e.g., results of diagnostic tests performed by actuators424-428), status information, commissioning information, configurationsettings, calibration data, and/or other types of information or datathat can be collected, stored, or used by actuators 424-428. AHUcontroller 430 can be an economizer controller configured to use one ormore control algorithms (e.g., state-based algorithms, extremum seekingcontrol (ESC) algorithms, proportional-integral (PI) control algorithms,proportional-integral-derivative (PID) control algorithms, modelpredictive control (MPC) algorithms, feedback control algorithms, etc.)to control actuators 424-428.

Still referring to FIG. 4, AHU 304 is shown to include a cooling coil434, a heating coil 436, and a fan 438 positioned within supply air duct412. Fan 438 can be configured to force supply air 410 through coolingcoil 434 and/or heating coil 436 and provide supply air 410 to buildingzone 406. AHU controller 430 may communicate with fan 438 viacommunications link 440 to control a flow rate of supply air 410. Insome embodiments, AHU controller 430 controls an amount of heating orcooling applied to supply air 410 by modulating a speed of fan 438.

Cooling coil 434 may receive a chilled fluid from waterside system 300(e.g., from cold water loop 316) via piping 442 and may return thechilled fluid to waterside system 300 via piping 444. Valve 446 can bepositioned along piping 442 or piping 444 to control a flow rate of thechilled fluid through cooling coil 434. In some embodiments, coolingcoil 434 includes multiple stages of cooling coils that can beindependently activated and deactivated (e.g., by AHU controller 430, byBMS controller 466, etc.) to modulate an amount of cooling applied tosupply air 410.

Heating coil 436 may receive a heated fluid from waterside system 300(e.g., from hot water loop 314) via piping 448 and may return the heatedfluid to waterside system 300 via piping 450. Valve 452 can bepositioned along piping 448 or piping 450 to control a flow rate of theheated fluid through heating coil 436. In some embodiments, heating coil436 includes multiple stages of heating coils that can be independentlyactivated and deactivated (e.g., by AHU controller 430, by BMScontroller 466, etc.) to modulate an amount of heating applied to supplyair 410.

Each of valves 446 and 452 can be controlled by an actuator. Forexample, valve 446 can be controlled by actuator 454 and valve 452 canbe controlled by actuator 456. Actuators 454-456 may communicate withAHU controller 430 via communications links 458-460. Actuators 454-456may receive control signals from AHU controller 430 and may providefeedback signals to controller 430. In some embodiments, AHU controller430 receives a measurement of the supply air temperature from atemperature sensor 462 positioned in supply air duct 412 (e.g.,downstream of cooling coil 434 and/or heating coil 436). AHU controller430 may also receive a measurement of the temperature of building zone406 from a temperature sensor 464 located in building zone 406.

In some embodiments, AHU controller 430 operates valves 446 and 452 viaactuators 454-456 to modulate an amount of heating or cooling providedto supply air 410 (e.g., to achieve a setpoint temperature for supplyair 410 or to maintain the temperature of supply air 410 within asetpoint temperature range). The positions of valves 446 and 452 affectthe amount of heating or cooling provided to supply air 410 by coolingcoil 434 or heating coil 436 and may correlate with the amount of energyconsumed to achieve a desired supply air temperature. AHU controller 430may control the temperature of supply air 410 and/or building zone 406by activating or deactivating coils 434-436, adjusting a speed of fan438, or a combination of both.

Still referring to FIG. 4, airside system 400 is shown to include abuilding management system (BMS) controller 466 and a client device 468.BMS controller 466 can include one or more computer systems (e.g.,servers, supervisory controllers, subsystem controllers, etc.) thatserve as system level controllers, application or data servers, headnodes, or master controllers for airside system 400, waterside system300, HVAC system 200, and/or other controllable systems that servebuilding 10. BMS controller 466 may communicate with multiple downstreambuilding systems or subsystems (e.g., HVAC system 200, a securitysystem, a lighting system, waterside system 300, etc.) via acommunications link 470 according to like or disparate protocols (e.g.,LON, BACnet, etc.). In various embodiments, AHU controller 430 and BMScontroller 466 can be separate (as shown in FIG. 4) or integrated. In anintegrated implementation, AHU controller 430 can be a software moduleconfigured for execution by a processor of BMS controller 466.

In some embodiments, AHU controller 430 receives information from BMScontroller 466 (e.g., commands, setpoints, operating boundaries, etc.)and provides information to BMS controller 466 (e.g., temperaturemeasurements, valve or actuator positions, operating statuses,diagnostics, etc.). For example, AHU controller 430 may provide BMScontroller 466 with temperature measurements from temperature sensors462-464, equipment on/off states, equipment operating capacities, and/orany other information that can be used by BMS controller 466 to monitoror control a variable state or condition within building zone 406.

Client device 468 can include one or more human-machine interfaces orclient interfaces (e.g., graphical user interfaces, reportinginterfaces, text-based computer interfaces, client-facing web services,web servers that provide pages to web clients, etc.) for controlling,viewing, or otherwise interacting with HVAC system 200, its subsystems,and/or devices. Client device 468 can be a computer workstation, aclient terminal, a remote or local interface, or any other type of userinterface device. Client device 468 can be a stationary terminal or amobile device. For example, client device 468 can be a desktop computer,a computer server with a user interface, a laptop computer, a tablet, asmartphone, a PDA, or any other type of mobile or non-mobile device.Client device 468 may communicate with BMS controller 466 and/or AHUcontroller 430 via communications link 472.

Building Management System

Referring now to FIG. 5, a block diagram of a building management system(BMS) 500 is shown, according to some embodiments. BMS 500 can beimplemented in building 10 to automatically monitor and control variousbuilding functions. BMS 500 is shown to include BMS controller 466 andbuilding subsystems 528. Building subsystems 528 are shown to include abuilding electrical subsystem 534, an information communicationtechnology (ICT) subsystem 536, a security subsystem 538, a HVACsubsystem 540, a lighting subsystem 542, a lift/escalators subsystem532, and a fire safety subsystem 530. In various embodiments, buildingsubsystems 528 can include fewer, additional, or alternative subsystems.For example, building subsystems 528 may also or alternatively include arefrigeration subsystem, an advertising or signage subsystem, a cookingsubsystem, a vending subsystem, a printer or copy service subsystem, orany other type of building subsystem that uses controllable equipmentand/or sensors to monitor or control building 10. In some embodiments,building subsystems 528 include waterside system 300 and/or airsidesystem 400, as described with reference to FIGS. 3-4.

Each of building subsystems 528 can include any number of devices (e.g.,IoT devices), sensors, controllers, and connections for completing itsindividual functions and control activities. HVAC subsystem 540 caninclude many of the same components as HVAC system 200, as describedwith reference to FIGS. 2-4. For example, HVAC subsystem 540 can includea chiller, a boiler, any number of air handling units, economizers,field controllers, supervisory controllers, actuators, temperaturesensors, and other devices for controlling the temperature, humidity,airflow, or other variable conditions within building 10. Lightingsubsystem 542 can include any number of light fixtures, ballasts,lighting sensors, dimmers, or other devices configured to controllablyadjust the amount of light provided to a building space. Securitysubsystem 538 can include occupancy sensors, video surveillance cameras,digital video recorders, video processing servers, intrusion detectiondevices, access control devices and servers, or other security-relateddevices.

Still referring to FIG. 5, BMS controller 466 is shown to include acommunications interface 507 and a BMS interface 509. Interface 507 mayfacilitate communications between BMS controller 466 and externalapplications (e.g., monitoring and reporting applications 522,enterprise control applications 526, remote systems and applications544, applications residing on client devices 548, 3^(rd) party services550, etc.) for allowing user control, monitoring, and adjustment to BMScontroller 466 and/or subsystems 528. Interface 507 may also facilitatecommunications between BMS controller 466 and client devices 548. BMSinterface 509 may facilitate communications between BMS controller 466and building subsystems 528 (e.g., HVAC, lighting security, lifts, powerdistribution, business, etc.).

Interfaces 507, 509 can be or include wired or wireless communicationsinterfaces (e.g., jacks, antennas, transmitters, receivers,transceivers, wire terminals, etc.) for conducting data communicationswith building subsystems 528 or other external systems or devices. Invarious embodiments, communications via interfaces 507, 509 can bedirect (e.g., local wired or wireless communications) or via acommunications network 546 (e.g., a WAN, the Internet, a cellularnetwork, etc.). For example, interfaces 507, 509 can include an Ethernetcard and port for sending and receiving data via an Ethernet-basedcommunications link or network. In another example, interfaces 507, 509can include a Wi-Fi transceiver for communicating via a wirelesscommunications network. In another example, one or both of interfaces507, 509 can include cellular or mobile phone communicationstransceivers. In one embodiment, communications interface 507 is a powerline communications interface and BMS interface 509 is an Ethernetinterface. In other embodiments, both communications interface 507 andBMS interface 509 are Ethernet interfaces or are the same Ethernetinterface.

Still referring to FIG. 5, BMS controller 466 is shown to include aprocessing circuit 504 including a processor 506 and memory 508.Processing circuit 504 can be communicably connected to BMS interface509 and/or communications interface 507 such that processing circuit 504and the various components thereof can send and receive data viainterfaces 507, 509. Processor 506 can be implemented as a generalpurpose processor, an application specific integrated circuit (ASIC),one or more field programmable gate arrays (FPGAs), a group ofprocessing components, or other suitable electronic processingcomponents.

Memory 508 (e.g., memory, memory unit, storage device, etc.) can includeone or more devices (e.g., RAM, ROM, Flash memory, hard disk storage,etc.) for storing data and/or computer code for completing orfacilitating the various processes, layers and modules described in thepresent application. Memory 508 can be or include volatile memory ornon-volatile memory. Memory 508 can include database components, objectcode components, script components, or any other type of informationstructure for supporting the various activities and informationstructures described in the present application. According to someembodiments, memory 508 is communicably connected to processor 506 viaprocessing circuit 504 and includes computer code for executing (e.g.,by processing circuit 504 and/or processor 506) one or more processesdescribed herein.

In some embodiments, BMS controller 466 is implemented within a singlecomputer (e.g., one server, one housing, etc.). In various otherembodiments BMS controller 466 can be distributed across multipleservers or computers (e.g., that can exist in distributed locations).Further, while FIG. 4 shows applications 522 and 526 as existing outsideof BMS controller 466, in some embodiments, applications 522 and 526 canbe hosted within BMS controller 466 (e.g., within memory 508).

Still referring to FIG. 5, memory 508 is shown to include an enterpriseintegration layer 510, an automated measurement and validation (AM&V)layer 512, a demand response (DR) layer 514, a fault detection anddiagnostics (FDD) layer 516, an integrated control layer 518, and abuilding subsystem integration later 520. Layers 510-520 can beconfigured to receive inputs from building subsystems 528 and other datasources, determine improved and/or optimal control actions for buildingsubsystems 528 based on the inputs, generate control signals based onthe improved and/or optimal control actions, and provide the generatedcontrol signals to building subsystems 528. The following paragraphsdescribe some of the general functions performed by each of layers510-520 in BMS 500.

Enterprise integration layer 510 can be configured to serve clients orlocal applications with information and services to support a variety ofenterprise-level applications. For example, enterprise controlapplications 526 can be configured to provide subsystem-spanning controlto a graphical user interface (GUI) or to any number of enterprise-levelbusiness applications (e.g., accounting systems, user identificationsystems, etc.). Enterprise control applications 526 may also oralternatively be configured to provide configuration GUIs forconfiguring BMS controller 466. In yet other embodiments, enterprisecontrol applications 526 can work with layers 510-520 to improve and/oroptimize building performance (e.g., efficiency, energy use, comfort, orsafety) based on inputs received at interface 507 and/or BMS interface509.

Building subsystem integration layer 520 can be configured to managecommunications between BMS controller 466 and building subsystems 528.For example, building subsystem integration layer 520 may receive sensordata and input signals from building subsystems 528 and provide outputdata and control signals to building subsystems 528. Building subsystemintegration layer 520 may also be configured to manage communicationsbetween building subsystems 528. Building subsystem integration layer520 translates communications (e.g., sensor data, input signals, outputsignals, etc.) across multi-vendor/multi-protocol systems.

Demand response layer 514 can be configured to determine (e.g.,optimize) resource usage (e.g., electricity use, natural gas use, wateruse, etc.) and/or the monetary cost of such resource usage to satisfythe demand of building 10. The resource usage determination can be basedon time-of-use prices, curtailment signals, energy availability, orother data received from utility providers, distributed energygeneration systems 524, energy storage 527 (e.g., hot TES 342, cold TES344, etc.), or from other sources. Demand response layer 514 may receiveinputs from other layers of BMS controller 466 (e.g., building subsystemintegration layer 520, integrated control layer 518, etc.). The inputsreceived from other layers can include environmental or sensor inputssuch as temperature, carbon dioxide levels, relative humidity levels,air quality sensor outputs, occupancy sensor outputs, room schedules,and the like. The inputs may also include inputs such as electrical use(e.g., expressed in kWh), thermal load measurements, pricinginformation, projected pricing, smoothed pricing, curtailment signalsfrom utilities, and the like.

According to some embodiments, demand response layer 514 includescontrol logic for responding to the data and signals it receives. Theseresponses can include communicating with the control algorithms inintegrated control layer 518, changing control strategies, changingsetpoints, or activating/deactivating building equipment or subsystemsin a controlled manner. Demand response layer 514 may also includecontrol logic configured to determine when to utilize stored energy. Forexample, demand response layer 514 may determine to begin using energyfrom energy storage 527 just prior to the beginning of a peak use hour.

In some embodiments, demand response layer 514 includes a control moduleconfigured to actively initiate control actions (e.g., automaticallychanging setpoints) which reduce (e.g., minimize) energy costs based onone or more inputs representative of or based on demand (e.g., price, acurtailment signal, a demand level, etc.). In some embodiments, demandresponse layer 514 uses equipment models to determine an improved and/oroptimal set of control actions. The equipment models can include, forexample, thermodynamic models describing the inputs, outputs, and/orfunctions performed by various sets of building equipment. Equipmentmodels may represent collections of building equipment (e.g., subplants,chiller arrays, etc.) or individual devices (e.g., individual chillers,heaters, pumps, etc.).

Demand response layer 514 may further include or draw upon one or moredemand response policy definitions (e.g., databases, XML files, etc.).The policy definitions can be edited or adjusted by a user (e.g., via agraphical user interface) so that the control actions initiated inresponse to demand inputs can be tailored for the user's application,desired comfort level, particular building equipment, or based on otherconcerns. For example, the demand response policy definitions canspecify which equipment can be turned on or off in response toparticular demand inputs, how long a system or piece of equipment shouldbe turned off, what setpoints can be changed, what the allowable setpoint adjustment range is, how long to hold a high demand setpointbefore returning to a normally scheduled setpoint, how close to approachcapacity limits, which equipment modes to utilize, the energy transferrates (e.g., the maximum rate, an alarm rate, other rate boundaryinformation, etc.) into and out of energy storage devices (e.g., thermalstorage tanks, battery banks, etc.), and when to dispatch on-sitegeneration of energy (e.g., via fuel cells, a motor generator set,etc.).

Integrated control layer 518 can be configured to use the data input oroutput of building subsystem integration layer 520 and/or demandresponse later 514 to make control decisions. Due to the subsystemintegration provided by building subsystem integration layer 520,integrated control layer 518 can integrate control activities of thesubsystems 528 such that the subsystems 528 behave as a singleintegrated super system. In some embodiments, integrated control layer518 includes control logic that uses inputs and outputs from buildingsubsystems to provide greater comfort and energy savings relative to thecomfort and energy savings that separate subsystems could provide alone.For example, integrated control layer 518 can be configured to use aninput from a first subsystem to make an energy-saving control decisionfor a second subsystem. Results of these decisions can be communicatedback to building subsystem integration layer 520.

Integrated control layer 518 is shown to be logically below demandresponse layer 514. Integrated control layer 518 can be configured toenhance the effectiveness of demand response layer 514 by enablingbuilding subsystems 528 and their respective control loops to becontrolled in coordination with demand response layer 514. Thisconfiguration may advantageously reduce disruptive demand responsebehavior relative to conventional systems. For example, integratedcontrol layer 518 can be configured to assure that a demandresponse-driven upward adjustment to the setpoint for chilled watertemperature (or another component that directly or indirectly affectstemperature) does not result in an increase in fan energy (or otherenergy used to cool a space) that would result in greater total buildingenergy use than was saved at the chiller.

Integrated control layer 518 can be configured to provide feedback todemand response layer 514 so that demand response layer 514 checks thatconstraints (e.g., temperature, lighting levels, etc.) are properlymaintained even while demanded load shedding is in progress. Theconstraints may also include setpoint or sensed boundaries relating tosafety, equipment operating limits and performance, comfort, fire codes,electrical codes, energy codes, and the like. Integrated control layer518 is also logically below fault detection and diagnostics layer 516and automated measurement and validation layer 512. Integrated controllayer 518 can be configured to provide calculated inputs (e.g.,aggregations) to these higher levels based on outputs from more than onebuilding subsystem.

Automated measurement and validation (AM&V) layer 512 can be configuredto verify that control strategies commanded by integrated control layer518 or demand response layer 514 are working properly (e.g., using dataaggregated by AM&V layer 512, integrated control layer 518, buildingsubsystem integration layer 520, FDD layer 516, or otherwise). Thecalculations made by AM&V layer 512 can be based on building systemenergy models and/or equipment models for individual BMS devices orsubsystems. For example, AM&V layer 512 may compare a model-predictedoutput with an actual output from building subsystems 528 to determinean accuracy of the model.

Fault detection and diagnostics (FDD) layer 516 can be configured toprovide on-going fault detection for building subsystems 528, buildingsubsystem devices (i.e., building equipment), and control algorithmsused by demand response layer 514 and integrated control layer 518. FDDlayer 516 may receive data inputs from integrated control layer 518,directly from one or more building subsystems or devices, or fromanother data source. FDD layer 516 may automatically diagnose andrespond to detected faults. The responses to detected or diagnosedfaults can include providing an alert message to a user, a maintenancescheduling system, or a control algorithm configured to attempt torepair the fault or to work-around the fault.

FDD layer 516 can be configured to output a specific identification ofthe faulty component or cause of the fault (e.g., loose damper linkage)using detailed subsystem inputs available at building subsystemintegration layer 520. In other exemplary embodiments, FDD layer 516 isconfigured to provide “fault” events to integrated control layer 518which executes control strategies and policies in response to thereceived fault events. According to some embodiments, FDD layer 516 (ora policy executed by an integrated control engine or business rulesengine) may shut-down systems or direct control activities around faultydevices or systems to reduce energy waste, extend equipment life, orassure proper control response.

FDD layer 516 can be configured to store or access a variety ofdifferent system data stores (or data points for live data). FDD layer516 may use some content of the data stores to identify faults at theequipment level (e.g., specific chiller, specific AHU, specific terminalunit, etc.) and other content to identify faults at component orsubsystem levels. For example, building subsystems 528 may generatetemporal (i.e., time-series) data indicating the performance of BMS 500and the various components thereof. The data generated by buildingsubsystems 528 can include measured or calculated values that exhibitstatistical characteristics and provide information about how thecorresponding system or process (e.g., a temperature control process, aflow control process, etc.) is performing in terms of error from itssetpoint. These processes can be examined by FDD layer 516 to exposewhen the system begins to degrade in performance and alert a user torepair the fault before it becomes more severe.

Building Management System with Cloud Building Management Platform

Referring now to FIG. 6, a block diagram of another building managementsystem (BMS) 600 is shown, according to some embodiments. BMS 600 can beconfigured to collect data samples from client devices 548, remotesystems and applications 544, 3^(rd) party services 550, and/or buildingsubsystems 528, and provide the data samples to Cloud buildingmanagement platform 620 to generate raw timeseries data, derivedtimeseries data, and/or entity data from the data samples. In someembodiments, Cloud building management platform 620 may supplement orreplace building management platform 102 shown in FIG. 1 or can beimplemented separate from building management platform 102. Cloudbuilding management platform 620 can process and transform the datasamples to generate derived timeseries data. Throughout this disclosure,the term “derived timeseries data” is used to describe the result oroutput of a transformation or other timeseries processing operationperformed by various services of the building management platform 620(e.g., data aggregation, data cleansing, virtual point calculation,etc.). The term “entity data” is used to describe the attributes ofvarious smart entities (e.g., IoT systems, devices, components, sensors,and the like) and the relationships between the smart entities. Thederived timeseries data can be provided to various applications 630and/or stored in storage 614 (e.g., as materialized views of the rawtimeseries data). In some embodiments, Cloud building managementplatform 620 separates data collection; data storage, retrieval, andanalysis; and data visualization into three different layers. Thisallows Cloud building management platform 620 to support a variety ofapplications 630 that use the derived timeseries data and allows newapplications 630 to reuse the existing infrastructure provided by Cloudbuilding management platform 620.

It should be noted that the components of BMS 600 and/or Cloud buildingmanagement platform 620 can be integrated within a single device (e.g.,a supervisory controller, a BMS controller, etc.) or distributed acrossmultiple separate systems or devices. In other embodiments, some or allof the components of BMS 600 and/or Cloud building management platform620 can be implemented as part of a cloud-based computing systemconfigured to receive and process data from one or more buildingmanagement systems. In other embodiments, some or all of the componentsof BMS 600 and/or Cloud building management platform 620 can becomponents of a subsystem level controller (e.g., a HVAC controller), asubplant controller, a device controller (e.g., AHU controller 330, achiller controller, etc.), a field controller, a computer workstation, aclient device, or any other system or device that receives and processesdata from building systems and equipment.

BMS 600 (or cloud building management platform 620) can include many ofthe same components as BMS 500 (e.g., processing circuit 504, processor506, and/or memory 508), as described with reference to FIG. 5. Forexample, BMS 600 is shown to include a communications interface 602(including the BMS interface 509 and the communications interface 507from FIG. 5). Interface 602 can include wired or wireless communicationsinterfaces (e.g., jacks, antennas, transmitters, receivers,transceivers, wire terminals, etc.) for conducting data communicationswith client devices 548, remote systems and applications 544, 3^(rd)party services 550, building subsystems 528 or other external systems ordevices. Communications conducted via interface 602 can be direct (e.g.,local wired or wireless communications) or via a communications network546 (e.g., a WAN, the Internet, a cellular network, etc.).

Communications interface 602 can facilitate communications between BMS600, Cloud building management platform services 620, buildingsubsystems 528, client devices 548 and external applications (e.g.,remote systems and applications 544 and 3^(rd) party services 550) forallowing user control, monitoring, and adjustment to BMS 600. BMS 600can be configured to communicate with building subsystems 528 using anyof a variety of building automation systems protocols (e.g., BACnet,Modbus, ADX, etc.). In some embodiments, BMS 600 receives data samplesfrom building subsystems 528 and provides control signals to buildingsubsystems 528 via interface 602. In some embodiments, BMS 600 receivesdata samples from the 3^(rd) party services 550, such as, for example,weather data from a weather service, news data from a news service,documents and other document-related data from a document service, media(e.g., video, images, audio, social media, etc.) from a media service,and/or the like, via interface 602 (e.g., via APIs or any suitableinterface).

Building subsystems 528 can include building electrical subsystem 534,information communication technology (ICT) subsystem 536, securitysubsystem 538, HVAC subsystem 540, lighting subsystem 542,lift/escalators subsystem 532, and/or fire safety subsystem 530, asdescribed with reference to FIG. 5. In various embodiments, buildingsubsystems 528 can include fewer, additional, or alternative subsystems.For example, building subsystems 528 can also or alternatively include arefrigeration subsystem, an advertising or signage subsystem, a cookingsubsystem, a vending subsystem, a printer or copy service subsystem, orany other type of building subsystem that uses controllable equipmentand/or sensors to monitor or control building 10. In some embodiments,building subsystems 528 include waterside system 300 and/or airsidesystem 400, as described with reference to FIGS. 3-4. Each of buildingsubsystems 528 can include any number of devices, controllers, andconnections for completing its individual functions and controlactivities. Building subsystems 528 can include building equipment(e.g., sensors, air handling units, chillers, pumps, valves, etc.)configured to monitor and control a building condition such astemperature, humidity, airflow, etc.

Still referring to FIG. 6, BMS 600 is shown to include a processingcircuit 606 including a processor 608 and memory 610. Cloud buildingmanagement platform 620 may include one or more processing circuitsincluding one or more processors and memory. Each of the processor canbe a general purpose or specific purpose processor, an applicationspecific integrated circuit (ASIC), one or more field programmable gatearrays (FPGAs), a group of processing components, or other suitableprocessing components. Each of the processors is configured to executecomputer code or instructions stored in memory or received from othercomputer readable media (e.g., CDROM, network storage, a remote server,etc.).

Memory can include one or more devices (e.g., memory units, memorydevices, storage devices, etc.) for storing data and/or computer codefor completing and/or facilitating the various processes described inthe present disclosure. Memory can include random access memory (RAM),read-only memory (ROM), hard drive storage, temporary storage,non-volatile memory, flash memory, optical memory, or any other suitablememory for storing software objects and/or computer instructions. Memorycan include database components, object code components, scriptcomponents, or any other type of information structure for supportingthe various activities and information structures described in thepresent disclosure. Memory can be communicably connected to theprocessors via the processing circuits and can include computer code forexecuting (e.g., by processor 508) one or more processes describedherein.

Still referring to FIG. 6, Cloud building management platform 620 isshown to include a data collector 612. Data collector 612 is shownreceiving data samples from 3^(rd) party services 550 and buildingsubsystems 528 via interface 602. However, the present disclosure is notlimited thereto, and the data collector 612 may receive the data samplesdirectly from the 3^(rd) party service 550 or the building subsystems528 (e.g., via network 546 or via any suitable method). In someembodiments, the data samples include data values for various datapoints. The data values can be measured and/or calculated values,depending on the type of data point. For example, a data point receivedfrom a temperature sensor can include a measured data value indicating atemperature measured by the temperature sensor. A data point receivedfrom a chiller controller can include a calculated data value indicatinga calculated efficiency of the chiller. A data sample received from a3^(rd) party weather service can include both a measured data value(e.g., current temperature) and a calculated data value (e.g., forecasttemperature). Data collector 612 can receive data samples from multipledifferent devices (e.g., IoT devices, sensors, etc.) within buildingsubsystems 528, and from multiple different 3^(rd) party services (e.g.,weather data from a weather service, news data from a news service,etc.) of the 3^(rd) party services 550.

The data samples can include one or more attributes that describe orcharacterize the corresponding data points. For example, the datasamples can include a name attribute defining a point name or ID (e.g.,“B1F4R2.T-Z”), a device attribute indicating a type of device from whichthe data samples is received (e.g., temperature sensor, humidity sensor,chiller, etc.), a unit attribute defining a unit of measure associatedwith the data value (e.g., ° F., ° C., kPA, etc.), and/or any otherattribute that describes the corresponding data point or providescontextual information regarding the data point. The types of attributesincluded in each data point can depend on the communications protocolused to send the data samples to BMS 600 and/or Cloud buildingmanagement platform 620. For example, data samples received via the ADXprotocol or BACnet protocol can include a variety of descriptiveattributes along with the data value, whereas data samples received viathe Modbus protocol may include a lesser number of attributes (e.g.,only the data value without any corresponding attributes).

In some embodiments, each data sample is received with a timestampindicating a time at which the corresponding data value was measured orcalculated. In other embodiments, data collector 612 adds timestamps tothe data samples based on the times at which the data samples arereceived. Data collector 612 can generate raw timeseries data for eachof the data points for which data samples are received. Each timeseriescan include a series of data values for the same data point and atimestamp for each of the data values. For example, a timeseries for adata point provided by a temperature sensor can include a series oftemperature values measured by the temperature sensor and thecorresponding times at which the temperature values were measured. Anexample of a timeseries which can be generated by data collector 612 isas follows:

-   -   [<key, timestamp₁, value₁>, <key, timestamp₂, value₂>, <key,        timestamp₃, value₃>]        where key is an identifier of the source of the raw data samples        (e.g., timeseries ID, sensor ID, device ID, etc.), timestamp_(i)        identifies the time at which the ith sample was collected, and        value_(i) indicates the value of the ith sample.

Data collector 612 can add timestamps to the data samples or modifyexisting timestamps such that each data sample includes a localtimestamp. Each local timestamp indicates the local time at which thecorresponding data sample was measured or collected and can include anoffset relative to universal time. The local timestamp indicates thelocal time at the location the data point was measured at the time ofmeasurement. The offset indicates the difference between the local timeand a universal time (e.g., the time at the international date line).For example, a data sample collected in a time zone that is six hoursbehind universal time can include a local timestamp (e.g.,Timestamp=2016-03-18T14:10:02) and an offset indicating that the localtimestamp is six hours behind universal time (e.g., Offset=−6:00). Theoffset can be adjusted (e.g., +1:00 or −1:00) depending on whether thetime zone is in daylight savings time when the data sample is measuredor collected.

The combination of the local timestamp and the offset provides a uniquetimestamp across daylight saving time boundaries. This allows anapplication using the timeseries data to display the timeseries data inlocal time without first converting from universal time. The combinationof the local timestamp and the offset also provides enough informationto convert the local timestamp to universal time without needing to lookup a schedule of when daylight savings time occurs. For example, theoffset can be subtracted from the local timestamp to generate auniversal time value that corresponds to the local timestamp withoutreferencing an external database and without requiring any otherinformation.

In some embodiments, data collector 612 organizes the raw timeseriesdata. Data collector 612 can identify a system or device associated witheach of the data points. For example, data collector 612 can associate adata point with a temperature sensor, an air handler, a chiller, or anyother type of system or device. In some embodiments, a data entity maybe created for the data point, in which case, the data collector 612(e.g., via entity service) can associate the data point with the dataentity. In various embodiments, data collector uses the name of the datapoint, a range of values of the data point, statistical characteristicsof the data point, or other attributes of the data point to identify aparticular system or device associated with the data point. Datacollector 612 can then determine how that system or device relates tothe other systems or devices in the building site from entity data. Forexample, data collector 612 can determine that the identified system ordevice is part of a larger system (e.g., a HVAC system) or serves aparticular space (e.g., a particular building, a room or zone of thebuilding, etc.) from the entity data. In some embodiments, datacollector 612 uses or retrieves an entity graph (e.g., via entityservice 626) when organizing the timeseries data.

Data collector 612 can provide the raw timeseries data to the servicesof Cloud building management platform 620 and/or store the rawtimeseries data in storage 614. Storage 614 may be internal storage orexternal storage. For example, storage 614 can be internal storage withrelation to Cloud building management platform 620 and/or BMS 600,and/or may include a remote database, cloud-based data hosting, or otherremote data storage. Storage 614 can be configured to store the rawtimeseries data obtained by data collector 612, the derived timeseriesdata generated by Cloud building management platform 620, and/ordirected acyclic graphs (DAGs) used by Cloud building managementplatform 620 to process the timeseries data.

Still referring to FIG. 5, Cloud building management platform 620 canreceive the raw timeseries data from data collector 612 and/or retrievethe raw timeseries data from storage 614. Cloud building managementplatform 620 can include a variety of services configured to analyze,process, and transform the raw timeseries data. For example, Cloudbuilding management platform 620 is shown to include a security service622, an analytics service 624, an entity service 626, and a timeseriesservice 628. Security service 622 can assign security attributes to theraw timeseries data to ensure that the timeseries data are onlyaccessible to authorized individuals, systems, or applications. Securityservice 622 may include a messaging layer to exchange secure messageswith the entity service 626. In some embodiment, security service 622may provide permission data to entity service 626 so that entity service626 can determine the types of entity data that can be accessed by aparticular entity or device. Entity service 626 can assign entityinformation (or entity data) to the timeseries data to associate datapoints with a particular system, device, or space. Timeseries service628 and analytics service 624 can apply various transformations,operations, or other functions to the raw timeseries data to generatederived timeseries data.

In some embodiments, timeseries service 628 aggregates predefinedintervals of the raw timeseries data (e.g., quarter-hourly intervals,hourly intervals, daily intervals, monthly intervals, etc.) to generatenew derived timeseries of the aggregated values. These derivedtimeseries can be referred to as “data rollups” since they are condensedversions of the raw timeseries data. The data rollups generated bytimeseries service 628 provide an efficient mechanism for applications630 to query the timeseries data. For example, applications 630 canconstruct visualizations of the timeseries data (e.g., charts, graphs,etc.) using the pre-aggregated data rollups instead of the rawtimeseries data. This allows applications 630 to simply retrieve andpresent the pre-aggregated data rollups without requiring applications630 to perform an aggregation in response to the query. Since the datarollups are pre-aggregated, applications 630 can present the datarollups quickly and efficiently without requiring additional processingat query time to generate aggregated timeseries values.

In some embodiments, timeseries service 628 calculates virtual pointsbased on the raw timeseries data and/or the derived timeseries data.Virtual points can be calculated by applying any of a variety ofmathematical operations (e.g., addition, subtraction, multiplication,division, etc.) or functions (e.g., average value, maximum value,minimum value, thermodynamic functions, linear functions, nonlinearfunctions, etc.) to the actual data points represented by the timeseriesdata. For example, timeseries service 628 can calculate a virtual datapoint (pointID₃) by adding two or more actual data points (pointID₁ andpointID₂) (e.g., pointID₃=pointID₁+pointID₂). As another example,timeseries service 628 can calculate an enthalpy data point (pointID₄)based on a measured temperature data point (pointID₅) and a measuredpressure data point (pointID₆) (e.g., pointID₄=enthalpy(pointID₅,pointID₆)). The virtual data points can be stored as derived timeseriesdata.

Applications 630 can access and use the virtual data points in the samemanner as the actual data points. Applications 630 may not need to knowwhether a data point is an actual data point or a virtual data pointsince both types of data points can be stored as derived timeseries dataand can be handled in the same manner by applications 630. In someembodiments, the derived timeseries are stored with attributesdesignating each data point as either a virtual data point or an actualdata point. Such attributes allow applications 630 to identify whether agiven timeseries represents a virtual data point or an actual datapoint, even though both types of data points can be handled in the samemanner by applications 630. These and other features of timeseriesservice 628 are described in greater detail with reference to FIG. 9.

In some embodiments, analytics service 624 analyzes the raw timeseriesdata and/or the derived timeseries data to detect faults. Analyticsservice 624 can apply a set of fault detection rules to the timeseriesdata to determine whether a fault is detected at each interval of thetimeseries. Fault detections can be stored as derived timeseries data.For example, analytics service 624 can generate a new fault detectiontimeseries with data values that indicate whether a fault was detectedat each interval of the timeseries. The fault detection timeseries canbe stored as derived timeseries data along with the raw timeseries datain storage 614.

In some embodiments, analytics service 624 analyzes the raw timeseriesdata and/or the derived timeseries data with the entity data to generatealerts or warnings, analyze risks, and determine threats. For example,analytics service 624 can apply probabilistic machine learning methodsto model risks associated with an asset. An asset may be any resource orentity type, such as, for example, a person, building, space, system,equipment, device, sensor, and the like. Analytics service 624 cangenerate a risk score associated with an asset based on modelparameters. The model parameters can be automatically updated based onfeedback on the accuracy of the risk predictions. For example, thefeedback may be explicit (e.g., based on questionnaires, disposition ofalerts, and the like) or implicit (e.g., analyzing user actions on eachthreat or alert to estimate the importance of a particular event, andthe like). The risk score may be stored as derived timeseries. Forexample, analytics service 624 (e.g., via timeseries service 628) cangenerate a risk score timeseries with data values indicating the riskscore at each interval of the timeseries. The risk score timeseries canbe stored as derived timeseries data along with the raw timeseries datain storage 614. The risk scores can then be retrieved, for example, by aRisk Dashboard from the timeseries service 628.

Still referring to FIG. 6, BMS 600 is shown to include severalapplications 630 including an energy management application 632,monitoring and reporting applications 634, and enterprise controlapplications 636. Although only a few applications 630 are shown, it iscontemplated that applications 630 can include any of a variety ofsuitable applications configured to use the raw or derived timeseriesgenerated by Cloud building management platform 620. In someembodiments, applications 630 exist as a separate layer of BMS 600(e.g., a part of Cloud building management platform 620 and/or datacollector 612). In other embodiments, applications 630 can exist asremote applications that run on remote systems or devices (e.g., remotesystems and applications 544, client devices 548, and/or the like).

Applications 630 can use the derived timeseries data to perform avariety data visualization, monitoring, and/or control activities. Forexample, energy management application 632 and monitoring and reportingapplication 634 can use the derived timeseries data to generate userinterfaces (e.g., charts, graphs, etc.) that present the derivedtimeseries data to a user. In some embodiments, the user interfacespresent the raw timeseries data and the derived data rollups in a singlechart or graph. For example, a dropdown selector can be provided toallow a user to select the raw timeseries data or any of the datarollups for a given data point.

Enterprise control application 636 can use the derived timeseries datato perform various control activities. For example, enterprise controlapplication 636 can use the derived timeseries data as input to acontrol algorithm (e.g., a state-based algorithm, an extremum seekingcontrol (ESC) algorithm, a proportional-integral (PI) control algorithm,a proportional-integral-derivative (PID) control algorithm, a modelpredictive control (MPC) algorithm, a feedback control algorithm, etc.)to generate control signals for building subsystems 528. In someembodiments, building subsystems 528 use the control signals to operatebuilding equipment. Operating the building equipment can affect themeasured or calculated values of the data samples provided to BMS 600and/or Cloud building management platform 620. Accordingly, enterprisecontrol application 636 can use the derived timeseries data as feedbackto control the systems and devices of building subsystems 528.

Cloud Building Management Platform Entity Service

Referring now to FIG. 7, a block diagram illustrating entity service 626in greater detail is shown, according to some embodiments. Entityservice 626 registers and manages various buildings (e.g., 110-140),spaces, persons, subsystems (e.g., 428), devices (e.g., 112-146), andother entities in the Cloud building management platform 620. Accordingto various embodiments, an entity may be any person, place, or physicalobject, hereafter referred to as an object entity. Further, an entitymay be any event, data point, or record structure, hereinafter referredto as data entity. In addition, an entity may define a relationshipbetween entities, hereinafter referred to as a relational entity.

In some embodiments, an object entity may be defined as having at leastthree types of attributes. For example, an object entity may have astatic attribute, a dynamic attribute, and a behavioral attribute. Thestatic attribute may include any unique identifier of the object entityor characteristic of the object entity that either does not change overtime or changes infrequently (e.g., a device ID, a person's name orsocial security number, a place's address or room number, and the like).The dynamic attribute may include a property of the object entity thatchanges over time (e.g., location, age, measurement, data point, and thelike). In some embodiments, the dynamic attribute of an object entitymay be linked to a data entity. In this case, the dynamic attribute ofthe object entity may simply refer to a location (e.g., data/networkaddress) or static attribute (e.g., identifier) of the linked dataentity, which may store the data (e.g., the value or information) of thedynamic attribute. Accordingly, in some such embodiments, when a newdata point (e.g., timeseries data) is received for the object entity,only the linked data entity may be updated, while the object entityremains unchanged. Therefore, resources that would have been expended toupdate the object entity may be reduced.

However, the present disclosure is not limited thereto. For example, insome embodiments, there may also be some data that is updated (e.g.,during predetermined intervals) in the dynamic attribute of the objectentity itself. For example, the linked data entity may be configured tobe updated each time a new data point is received, whereas thecorresponding dynamic attribute of the object entity may be configuredto be updated less often (e.g., at predetermined intervals less than theintervals during which the new data points are received). In someimplementations, the dynamic attribute of the object entity may includeboth a link to the data entity and either a portion of the data from thedata entity or data derived from the data of the data entity. Forexample, in an embodiment in which periodic temperature readings arereceived from a thermostat, an object entity corresponding to thethermostat could include the last temperature reading and a link to adata entity that stores a series of the last ten temperature readingsreceived from the thermostat.

The behavioral attribute may define a function of the object entity, forexample, based on inputs, capabilities, and/or permissions. For example,behavioral attributes may define the types of inputs that the objectentity is configured to accept, how the object entity is expected torespond under certain conditions, the types of functions that the objectentity is capable of performing, and the like. As a non-limitingexample, if the object entity represents a person, the behavioralattribute of the person may be his/her job title or job duties, userpermissions to access certain systems or locations, expected location orbehavior given a time of day, tendencies or preferences based onconnected activity data received by entity service 626 (e.g., socialmedia activity), and the like. As another non-limiting example, if theobject entity represents a device, the behavioral attributes may includethe types of inputs that the device can receive, the types of outputsthat the device can generate, the types of controls that the device iscapable of, the types of software or versions that the device currentlyhas, known responses of the device to certain types of input (e.g.,behavior of the device defined by its programming), and the like.

In some embodiments, the data entity may be defined as having at least astatic attribute and a dynamic attribute. The static attribute of thedata entity may include a unique identifier or description of the dataentity. For example, if the data entity is linked to a dynamic attributeof an object entity, the static attribute of the data entity may includean identifier that is used to link to the dynamic attribute of theobject entity. In some embodiments, the dynamic attribute of the dataentity represents the data for the dynamic attribute of the linkedobject entity. In some embodiments, the dynamic attribute of the dataentity may represent some other data that is derived, analyzed,inferred, calculated, or determined based on data from data sources.

In some embodiments, the relational entity may be defined as having atleast a static attribute. The static attribute of the relational entitymay semantically define the type of relationship between two or moreentities. For example, in a non-limiting embodiment, a relational entityfor a relationship that semantically defines that Entity A has a part ofEntity B, or that Entity B is a part of Entity A may include:

-   -   hasPart{Entity A, Entity B}        where the static attribute hasPart defines what the relationship        is of the listed entities, and the order of the listed entities        or data field of the relational entity specifies which entity is        the part of the other (e.g., Entity A→hasPart→Entity B).

In various embodiments, the relational entity is an object-orientedconstruct with predefined fields that define the relationship betweentwo or more entities, regardless of the type of entities. For example,Cloud building management platform 620 can provide a rich set ofpre-built entity models with standardized relational entities that canbe used to describe how any two or more entities are semanticallyrelated, as well as how data is exchanged and/or processed between theentities. Accordingly, a global change to a definition or relationshipof a relational entity at the system level can be effected at the objectlevel, without having to manually change the entity relationships foreach object or entity individually. Further, in some embodiments, aglobal change at the system level can be propagated through tothird-party applications integrated with Cloud building managementplatform 620 such that the global change can be implemented across allof the third-party applications without requiring manual implementationof the change in each disparate application.

For example, referring to FIG. 8, an example entity graph of entity datais shown, according to some embodiments. The term “entity data” is usedto describe the attributes of various entities and the relationshipsbetween the entities. For example, entity data may be represented in theform of an entity graph. In some embodiments, entity data includes anysuitable predefined data models (e.g., as a table, JSON data, and/or thelike), such as entity type or object, and further includes one or morerelational entities that semantically define the relationships betweenthe entities. The relational entities may help to semantically define,for example, hierarchical or directed relationships between the entities(e.g., entity X controls entity Y, entity A feeds entity B, entity 1 islocated in entity 2, and the like). For example, an object entity (e.g.,IoT device) may be represented by entity type or object, which generallydescribes how data corresponding to the entity will be structured andstored.

For example, an entity type (or object) “Thermostat” may be representedvia the below schema:

Thermostat{ Type, Model No, Device Name, Manufactured date, Serialnumber, MAC address, Location, Current air quality, Current indoortemperature, Current outdoor temperature, Target indoor temperature,Point schedule (e.g., BACnet schedule object) }where various attributes are static attributes (e.g., “Type,” “ModelNumber,” “Device Name,” etc.), dynamic attributes (e.g., “Current airquality,” “Current outdoor temperature,” etc.), or behavioral attributes(e.g., “Target indoor temperature,” etc.) for the object entity“thermostat.” In a relational database, the object “Thermostat” is atable name, and the attributes represents column names.

An example of an object entity data model for a person named John Smithin a relational database may be represented by the below table:

First Last Job Name Name Tel. No. Age Location Title John Smith(213)220-XXXX 36 Home Engineerwhere various attributes are static attributes (e.g., “First Name,”“Last Name,” etc.), dynamic attributes (e.g., “Age,” “Location,” etc.),or behavioral attributes (e.g., “Engineer”) for the object entity “JohnSmith.”

An example data entity for the data point “Current indoor temperature”for the “Thermostat” owned by John Smith in a relational database may berepresented by the below table:

Present- Unit of Value Description Device_Type measure 68 “Currentindoor Thermostat Degrees-F. temperature of John's house”where various attributes are static attributes (e.g., “Description” and“Device_Type”) and dynamic attributes (e.g., “Present-Value”).

While structuring the entities via entity type or object may help todefine the data representation of the entities, these data models do notprovide information on how the entities relate to each other. Forexample, a BMS, building subsystem, or device may need data from aplurality of sources as well as information on how the sources relate toeach other in order to provide a proper decision, action, orrecommendation. Accordingly, in various embodiments, the entity datafurther includes the relational entities to semantically define therelationships between the entities, which may help to increase speeds inanalyzing data, as well as provide ease of navigation and browsing.

For example, still referring to FIG. 8, an entity graph 800 for theThermostat object entity 802 includes various class entities (e.g.,User, Address, SetPoint Command, and Temperature Object), relationalentities (e.g., isAKindOf, Owns, isLinked, hasStorage, andhasOperation), and data entities (AI 201-01, TS ID 1, Daily Average 1,Abnormal indoor temp 1, AO 101-1, and Geo 301-01). The relationalentities describe the relationships between the various class, object,and data entities in a semantic and syntactic manner, so that anapplication or user viewing the entity graph 800 can quickly determinethe relationships and data process flow of the Thermostat object entity802, without having to resort to a data base analyst or engineer tocreate, index, and/or manage the entities (e.g., using SQL or NoSQL).

For example, the entity graph 800 shows that a person named John (objectentity) 804 isAKindOf (relational entity) 806 User (class entity) 808.John 804 Owns (relational entity) 810 the Thermostat 802. The Thermostat802 has a location attribute (dynamic attribute) 812 that isLinked(relational entity) 814 to Geo 301-01 (data entity) 816, which isAKindOf(relational entity) 818 an Address (class entity) 820. Accordingly, Geo301-01 316 should have a data point corresponding to an address.

The Thermostat 802 further includes a “Current indoor temperature”attribute (dynamic attribute) 822 that isLinked (relational entity) 824to AI 201-01 (data entity) 826. AI 201-01 826 isAKindOf (relationalentity) 828 Temperature Object (class entity) 830. Thus, AI 201-01 826should contain some sort of temperature related data. AI 201-01 826hasStorage (relational entity) 832 at TS ID 1 (data entity) 834, whichmay be raw or derived timeseries data for the temperature readings. AI201-01 826 hasOperation (relational entity) 836 of Daily Average 1 (dataentity) 838, which isAKindOf (relational entity) 840 Analytic Operator(class entity) 842. Thus, Daily Average 1 results from an analyticoperation that calculates the daily average of the indoor temperature.AI 201-01 826 further hasOperation (relational entity) 854 of AbnormalIndoor Temperature (data entity) 856, which isAKindOf (relationalentity) 858 Analytic Operator (class entity) 860. Accordingly, AbnormalIndoor Temperature results from an analytic operation to determine anabnormal temperature (e.g., exceeds or falls below a threshold value).

In this example, the data entity AI 201-01 526 may be represented by thefollowing data model:

point { name: “AI 201-01”; type: “analog input”; value: 72; unit:“Degree-F”; source: “Temperature Sensor 1” }where “point” is an example of a data entity that may be created byCloud building management platform 620 to hold the value for the linked“Current indoor temperature” 822 dynamic attribute of the Thermostatentity 802, and source is the sensor or device in the Thermostat devicethat provides the data to the linked “Current indoor temperature” 822dynamic attribute.

The data entity TS Id 1 534 may be represented, for example, by thefollowing data model:

timeseries { name: “TS Id 1”; type: “Daily Average”; values: “[68,20666, 70, 69, 71]; unit: “Degree-F”; point: “AI 201-01”; source: “DailyAverage 1” }where the data entity Daily Average 1 838 represents a specific analyticoperator used to create the data entity for the average daily timeseriesTS Id 1 834 based on the values of the corresponding data entity forpoint AI 201-01 826. The relational entity hasOperation shows that theAI 201-01 data entity 826 is used as an input to the specific logic/mathoperation represented by Daily Average 1 838. TS Id 1 834 might alsoinclude an attribute that identifies the analytic operator Daily Average1 838 as the source of the data samples in the timeseries.

Still referring to FIG. 8, the entity graph 800 for Thermostat 802 showsthat the “Target indoor temperature” attribute (dynamic attribute) 844isLinked (relational attribute) 846 to the data entity AO 101-01 (dataentity) 848. AO 101-01 data entity 848 isAKindOf (relational attribute)850 SetPoint Command (class entity) 852. Thus, the data in data entityAO 101-01 848 may be set via a command by the user or other entity, andmay be used to control the Thermostat object entity 802. Accordingly, invarious embodiments, entity graph 800 provides a user friendly view ofthe various relationships between the entities and data processing flow,which provides for ease of navigation, browsing, and analysis of data.

Referring again to FIG. 7, entity service 626 may transform raw datasamples and/or raw timeseries data into data corresponding to entitydata. For example, as discussed above with reference to FIG. 8, entityservice 626 can create data entities that use and/or represent datapoints in the timeseries data. Entity service 626 includes a web service702, a registration service 704, a management service 706, atransformation service 708, a search service 710, and storage 712. Insome embodiments, storage 712 may be internal storage or externalstorage. For example, storage 712 may be storage 614 (see FIG. 6),internal storage with relation to entity service 626, and/or may includea remote database, cloud-based data hosting, or other remote datastorage.

Web service 702 can be configured to interact with web-basedapplications to send entity data and/or receive raw data (e.g., datasamples, timeseries data, and the like). For example, web service 702can provide an interface (e.g., API, UI/UX, and the like) to manage(e.g., register, create, edit, delete, and/or update) an entity (e.g.,class entity, object entity, data entity, relational entity, and/or thelike). In some embodiments, web service 702 provides entity data toweb-based applications. For example, if one or more of applications 630are web-based applications, web service 702 can provide entity data tothe web-based applications. In some embodiments, web service 702receives raw data samples and/or raw timeseries data including deviceinformation from a web-based data collector, or a web-based securityservice to identify authorized entities and to exchange securedmessages. For example, if data collector 612 is a web-based application,web service 702 can receive the raw data samples and/or timeseries dataincluding a device attribute indicating a type of device (e.g., IoTdevice) from which the data samples and/or timeseries data are receivedfrom data collector 612. In some embodiments, web service 702 maymessage security service 622 to request authorization information and/orpermission information of a particular user, building, BMS, buildingsubsystem, device, application, or other entity. In some embodiments,web service 702 receives derived timeseries data from timeseries service628, and/or may provide entity data to timeseries service 628. In someembodiments, the entity service 626 processes and transforms thecollected data to generate the entity data.

The registration service 704 can perform registration of devices andentities. For example, registration service 704 can communicate withbuilding subsystems 528 and client devices 548 (e.g., via web service702) to register each entity (e.g., building, BMS, building subsystems,devices, and the like) with Cloud building management platform 620. Insome embodiments, registration service 704 registers a particularbuilding subsystem 528 (or the devices therein) with a specific userand/or a specific set of permissions and/or entitlements. For example, auser may register a device key and/or a device ID associated with thedevice via a web portal (e.g., web service 702). In some embodiments,the device ID and the device key may be unique to the device. The deviceID may be a unique number associated with the device such as a uniquealphanumeric string, a serial number of the device, and/or any otherstatic identifier. In various embodiments, the device is provisioned bya manufacturer and/or any other entity. In various embodiments, thedevice key and/or device ID are saved to the device or buildingsubsystem 528 based on whether the device includes a trusted platformmodule (TPM). If the device includes a TPM, the device or buildingsubsystem 528 may store the device key and/or device ID according to theprotocols of the TPM. If the device does not include a TPM, the deviceor building subsystem 528 may store the device key and/or device ID in afile and/or file field which may be stored in a secure storage location.Further, in some embodiments, the device ID may be stored with BIOSsoftware of the device. For example, a serial number of BIOS softwaremay become and/or may be updated with the device ID.

In various embodiments, the device key and/or the device ID are uploadedto registration service 704 (e.g., an IoT hub such as AZURE® IoT Hub).In some embodiments, registration service 704 is configured to store thedevice key and the device ID in secure permanent storage and/or may bestored by security service 622 (e.g., by a security API). In someembodiments, a manufacturer and/or any other individual may register thedevice key and the device ID with registration service 704 (e.g., viaweb service 702). In various embodiments, the device key and the deviceID are linked to a particular profile associated with the buildingsubsystem 528 or device and/or a particular user profile (e.g., aparticular user). In this regard, a device (or building subsystem 528)can be associated with a particular user. In various embodiments, thedevice key and the device ID make up the profile for device. The profilemay be registered as a device that has been manufactured and/orprovisioned but has not yet been purchased by an end user.

In various embodiments, registration service 704 adds and/or updates adevice in an building hub device registry. In various embodiments,registration service 704 may determine if the device is alreadyregistered, can set various authentication values (e.g., device ID,device key), and can update the building hub device registry. In asimilar manner, registration service 704 can update a document databasewith the various device registration information.

In some embodiments, registration service 704 can be configured tocreate a virtual representation (e.g., “digital twins” or “shadowrecords”) of each object entity (e.g., person, room, building subsystem,device, and the like) in the building within Cloud building managementplatform 620. In some embodiments, the virtual representations are smartentities that include attributes defining or characterizing thecorresponding object and are associated to the corresponding objectentity via relational entities defining the relationship of the objectand the smart entity representation thereof. In some embodiments, thevirtual representations maintain shadow copies of the object entitieswith versioning information so that entity service 626 can store notonly the most recent update of an attribute (e.g., a dynamic attribute)associated with the object, but records of previous states of theattributes (e.g., dynamic attributes) and/or entities. For example, theshadow record may be created as a type of data entity that is related toa linked data entity corresponding to the dynamic attribute of theobject entity (e.g., the person, room, building subsystem, device, andthe like). For example, the shadow entity may be associated with thelinked data entity via a relational entity (e.g., isLinked, hasStorage,hasOperation, and the like). In this case, the shadow entity may be usedto determine additional analytics for the data point of the dynamicattribute. For example, the shadow entity may be used to determine anaverage value, an expected value, or an abnormal value of the data pointfrom the dynamic attribute.

Management service 706 may create, modify, or update various attributes,data entities, and/or relational entities of the objects managed byentity service 626 for each entity rather than per class or type ofentity. This allows for separate processing/analytics for eachindividual entity rather than only to a class or type of entity. Someattributes (or data entities) may correspond to, for example, the mostrecent value of a data point provided to BMS 600 or Cloud buildingmanagement platform 620 via the raw data samples and/or timeseries data.For example, the “Current indoor temperature” dynamic attribute of the“Thermostat” object entity 802 in the example discussed above may be themost recent value of indoor temperature provided by the Thermostatdevice. Management service 706 can use the relational entities of theentity data for Thermostat to determine where to update the data of theattribute.

For example, Management service 706 may determine that a data entity(e.g., AI 201-01) is linked to the “Current indoor temperature” dynamicattribute of Thermostat via an isLinked relational entity. In this case,Management service 706 may automatically update the attribute data inthe linked data entity. Further, if a linked data entity does not exist,Management service 706 can create a data entity (e.g., AI 201-01) and aninstance of the isLinked relational entity 824 to store and link the“Current indoor temperature” dynamic attribute of Thermostat therein.Accordingly, processing/analytics for Thermostat 802 may be automated.As another example, a “most recent view” attribute (or linked dataentity) of a webpage object entity may indicate the most recent time atwhich the webpage was viewed. Management service 706 can use the entitydata from a related click tracking system object entity or web serverobject entity to determine when the most recent view occurred and canautomatically update the “most recent view” attribute (or linked dataentity) of the webpage entity accordingly.

Other data entities and/or attributes may be created and/or updated as aresult of an analytic, transformation, calculation, or other processingoperation based on the raw data and/or entity data. For example,Management service 706 can use the relational entities in entity data toidentify a related access control device (e.g., a card reader, a keypad,etc.) at the entrance/exit of a building object entity. Managementservice 706 can use raw data received from the identified access controldevice to track the number of occupants entering and exiting thebuilding object entity (e.g., via related card entities used by theoccupants to enter and exit the building). Management service 706 canupdate a “number of occupants” attribute (or corresponding data entity)of the building object each time a person enters or exits the buildingusing a related card entity, such that the “number of occupants”attribute (or data entity) reflects the current number of occupantswithin the building object. As another example, a “total revenue”attribute associated with a product line object may be the summation ofall the revenue generated from related point of sales entities.Management service 706 can use the raw data received from the relatedpoint of sales entities to determine when a sale of the product occurs,and can identify the amount of revenue generated by the sales.Management service 706 can then update the “total revenue” attribute (orrelated data entity) of the product line object by adding the mostrecent sales revenue from each of the related point of sales entities tothe previous value of the attribute.

In some embodiments, management service 706 may use derived timeseriesdata generated from timeseries service 628 to update or create a dataentity (e.g., Daily Average 1) that uses or stores the data points inthe derived timeseries data. For example, the derived timeseries datamay include a virtual data point corresponding to the daily averagesteps calculated by timeseries service 628, and management service 706may update the data entity or entities that store or use the datacorresponding to the virtual data point as determined via the relationalentities. In some embodiments, if a data entity corresponding to thevirtual data point does not exist, management service 706 mayautomatically create a corresponding data entity and one or morerelational entities that describe the relationship between thecorresponding data entity and other entities.

In some embodiments, management service 706 uses entity data and/or rawdata from multiple different data sources to update the attributes (orcorresponding data entities) of various object entities. For example, anobject entity representing a person (e.g., a person's cellular device orother related object entity) may include a “risk” attribute thatquantifies the person's level of risk attributable to various physical,environmental, or other conditions. Management service 706 can userelational entities of the person object entity to identify a relatedcard device and/or a related card reader from a related building objectentity (e.g., the building in which the person works) to determine thephysical location of the person at any given time. Management service706 can determine from raw data (e.g., time that the card device wasscanned by the card reader) or derived timeseries data (e.g., averagetime of arrival) whether the person object is located in the building ormay be in transit to the building. Management service 706 can associateweather data from a weather service in the region in which the buildingobject entity is located with the building object entity, and analyticsservice 624 can generate a risk score for the possibility that anysevere weather is approaching the person's location based on theassociated weather data, building entity, and person entity. Similarly,management service 706 can associate building data from related buildingentities with the building object entity, and analytics service 624 candetermine whether the building in which the person is located isexperiencing any emergency conditions (e.g., fire, building lockdown,etc.) or environmental hazards (e.g., detected air contaminants,pollutants, extreme temperatures, etc.) that could increase the person'slevel of risk. Management service 706 can provide these and other typesof data to analytics service 624 as inputs to a risk function thatcalculates the value of the person object's “risk” attribute and canupdate the person object (or related device entity of the person object)accordingly.

In some embodiments, management service 706 can be configured tosynchronize configuration settings, parameters, and otherdevice-specific or object-specific information between the entities andCloud building management platform 620. In some embodiments, thesynchronization occurs asynchronously. Management service 706 can beconfigured to manage device properties dynamically. The deviceproperties, configuration settings, parameters, and otherdevice-specific information can be synchronized between the smartentities created by and stored within Cloud building management platform620.

In some embodiments, management service 706 is configured to manage amanifest for each of the building subsystems 528 (or devices therein).The manifest may include a set of relationships between the buildingsubsystems 528 and various entities. Further, the manifest may indicatea set of entitlements for the building subsystems 528 and/orentitlements of the various entities and/or other entities. The set ofentitlements may allow a BMS 600, building subsystem 528 and/or a userto perform certain actions within the building or (e.g., control,configure, monitor, and/or the like).

Still referring to FIG. 7, transformation service 708 can provide datavirtualization, and can transform various predefined standard datamodels for entities in a same class or type to have the same entity datastructure, regardless of the object, device, or Thing that the entityrepresents. For example, each object entity under an object class mayinclude a location attribute, regardless of whether or not the locationattribute is used or even generated. Thus, if an application is laterdeveloped requiring that each object entity includes a locationattribute, manual mapping of heterogeneous data of different entities inthe same class may be avoided. Accordingly, interoperability andscalability of applications may be improved.

In some embodiments, transformation service 708 can provide entitymatching, cleansing, and correlation so that a unified cleansed view ofthe entity data including the entity related information (e.g.,relational entities) can be provided. Transformation service 708 cansupport semantic and syntactic relationship description in the form ofstandardized relational entities between the various entities. This maysimplify machine learning because the relational entities themselvesprovide all the relationship description between the other entities.Accordingly, the rich set of pre-built entity models and standardizedrelational entities may provide for rapid application development anddata analytics.

Still referring to FIG. 7, the search service 710 provides a unifiedview of product related information in the form of the entity graph,which correlates entity relationships (via relational entities) amongmultiple data sources (e.g., CRM, ERP, MRP and the like). In someembodiments, the search service 710 is based on a schema-less and graphbased indexing architecture. The search service 710 facilitates simplequeries without having to search multiple levels of the hierarchicaltree of the entity graph. For example, search service 710 can returnresults based on searching of entity type, individual entities,attributes, or even relational entities without requiring other levelsor entities of the hierarchy to be searched.

Timeseries Data Platform Service

Referring now to FIG. 9, a block diagram illustrating timeseries service628 in greater detail is shown, according to some embodiments.Timeseries service 628 is shown to include a timeseries web service 902,an events service 903, a timeseries processing engine 904, and atimeseries storage interface 916. Timeseries web service 902 can beconfigured to interact with web-based applications to send and/orreceive timeseries data. In some embodiments, timeseries web service 902provides timeseries data to web-based applications. For example, if oneor more of applications 630 are web-based applications, timeseries webservice 902 can provide derived timeseries data and/or raw timeseriesdata to the web-based applications. In some embodiments, timeseries webservice 902 receives raw timeseries data from a web-based datacollector. For example, if data collector 612 is a web-basedapplication, timeseries web service 902 can receive raw data samples orraw timeseries data from data collector 612. In some embodiments,timeseries web service 902 and entity service web service 702 may beintegrated as parts of the same web service.

Timeseries storage interface 916 can be configured to store and readsamples of various timeseries (e.g., raw timeseries data and derivedtimeseries data) and eventseries (described in greater detail below).Timeseries storage interface 916 can interact with storage 614. Forexample, timeseries storage interface 916 can retrieve timeseries datafrom a timeseries database 928 within storage 614. In some embodiments,timeseries storage interface 916 reads samples from a specified starttime or start position in the timeseries to a specified stop time or astop position in the timeseries. Similarly, timeseries storage interface916 can retrieve eventseries data from an eventseries database 929within storage 614. Timeseries storage interface 916 can also storetimeseries data in timeseries database 928 and can store eventseriesdata in eventseries database 929. Advantageously, timeseries storageinterface 916 provides a consistent interface which enables logical dataindependence.

In some embodiments, timeseries storage interface 916 stores timeseriesas lists of data samples, organized by time. For example, timeseriesstorage interface 916 can store timeseries in the following format:

[< key, timestamp₁, value₁ >, < key, timestamp₂, value₂ >, < key,timestamp₃, value₃ >]where key is an identifier of the source of the data samples (e.g.,timeseries ID, sensor ID, device ID, etc.), timestamp_(i) identifies atime associated with the ith sample, and value_(i) indicates the valueof the ith sample.

In some embodiments, timeseries storage interface 916 stores eventseriesas lists of events having a start time, an end time, and a state. Forexample, timeseries storage interface 916 can store eventseries in thefollowing format:

[< eventID₁, start_timestamp₁, end_timestamp₁, state₁ >,..., <eventID_(N), start_timestamp_(N), end_timestamp_(N), state_(N) >]where eventID_(i) is an identifier of the ith event, start_timestamp_(i)is the time at which the ith event started, end_timestamp_(i) is thetime at which the ith event ended, state_(i) describes a state orcondition associated with the ith event (e.g., cold, hot, warm, etc.),and N is the total number of events in the eventseries.

In some embodiments, timeseries storage interface 916 stores timeseriesand eventseries in a tabular format. Timeseries storage interface 916can store timeseries and eventseries in various tables having a columnfor each attribute of the timeseries/eventseries samples (e.g., key,timestamp, value). The timeseries tables can be stored in timeseriesdatabase 928, whereas the eventseries tables can be stored ineventseries database 929. In some embodiments, timeseries storageinterface 916 caches older data to storage 614 but stores newer data inRAM. This may improve read performance when the newer data are requestedfor processing.

In some embodiments, timeseries storage interface 916 omits one or moreof the attributes when storing the timeseries samples. For example,timeseries storage interface 916 may not need to repeatedly store thekey or timeseries ID for each sample in the timeseries. In someembodiments, timeseries storage interface 916 omits timestamps from oneor more of the samples. If samples of a particular timeseries havetimestamps at regular intervals (e.g., one sample each minute),timeseries storage interface 916 can organize the samples by timestampsand store the values of the samples in a row. The timestamp of the firstsample can be stored along with the interval between the timestamps.Timeseries storage interface 916 can determine the timestamp of anysample in the row based on the timestamp of the first sample and theposition of the sample in the row.

In some embodiments, timeseries storage interface 916 stores one or moresamples with an attribute indicating a change in value relative to theprevious sample value. The change in value can replace the actual valueof the sample when the sample is stored in timeseries database 928. Thisallows timeseries storage interface 916 to use fewer bits when storingsamples and their corresponding values. Timeseries storage interface 916can determine the value of any sample based on the value of the firstsample and the change in value of each successive sample.

In some embodiments, timeseries storage interface 916 invokes entityservice 626 to create data entities in which samples of timeseries dataand/or eventseries data can be stored. The data entities can includeJSON objects or other types of data objects to store one or moretimeseries samples and/or eventseries samples. Timeseries storageinterface 916 can be configured to add samples to the data entities andread samples from the data entities. For example, timeseries storageinterface 916 can receive a set of samples from data collector 612,entity service 626, timeseries web service 902, events service 903,and/or timeseries processing engine 904. Timeseries storage interface916 can add the set of samples to a data entity by sending the samplesto entity service 626 to be stored in the data entity, for example, ormay directly interface with the data entity to add/modify the sample tothe data entity.

Timeseries storage interface 916 can use data entities when readingsamples from storage 614. For example, timeseries storage interface 916can retrieve a set of samples from storage 614 or from entity service626, and add the samples to a data entity (e.g., directly or via entityservice 626). In some embodiments, the set of samples include allsamples within a specified time period (e.g., samples with timestamps inthe specified time period) or eventseries samples having a specifiedstate. Timeseries storage interface 916 can provide the samples in thedata entity to timeseries web service 902, events service 903,timeseries processing engine 904, applications 630, and/or othercomponents configured to use the timeseries/eventseries samples.

Still referring to FIG. 9, timeseries processing engine 904 is shown toinclude several timeseries operators 906. Timeseries operators 906 canbe configured to apply various operations, transformations, or functionsto one or more input timeseries to generate output timeseries and/oreventseries. The input timeseries can include raw timeseries data and/orderived timeseries data. Timeseries operators 906 can be configured tocalculate aggregate values, averages, or apply other mathematicaloperations to the input timeseries. In some embodiments, timeseriesoperators 906 generate virtual point timeseries by combining two or moreinput timeseries (e.g., adding the timeseries together), creatingmultiple output timeseries from a single input timeseries, or applyingmathematical operations to the input timeseries. In some embodiments,timeseries operators 906 perform data cleansing operations ordeduplication operations on an input timeseries. In some embodiments,timeseries operators 906 use the input timeseries to generateeventseries based on the values of the timeseries samples. The outputtimeseries can be stored as derived timeseries data in storage 614 asone or more timeseries data entities. Similarly, the eventseries can bestored as eventseries data entities in storage 614.

In some embodiments, timeseries operators 906 do not change or replacethe raw timeseries data, but rather generate various “views” of the rawtimeseries data (e.g., as separate data entities) with correspondingrelational entities defining the relationships between the rawtimeseries data entity and the various views data entities. The viewscan be queried in the same manner as the raw timeseries data. Forexample, samples can be read from the raw timeseries data entity,transformed to create the view entity, and then provided as an output.Because the transformations used to create the views can becomputationally expensive, the views can be stored as “materializedview” data entities in timeseries database 928. Instances of relationalentities can be created to define the relationship between the rawtimeseries data entity and the materialize view data entities. Thesematerialized views are referred to as derived data timeseries throughoutthe present disclosure.

Timeseries operators 906 can be configured to run at query time (e.g.,when a request for derived data timeseries is received) or prior toquery time (e.g., when new raw data samples are received, in response toa defined event or trigger, etc.). This flexibility allows timeseriesoperators 906 to perform some or all of their operations ahead of timeand/or in response to a request for specific derived data timeseries.For example, timeseries operators 906 can be configured to pre-processone or more timeseries that are read frequently to ensure that thetimeseries are updated whenever new data samples are received, and thepre-processed timeseries may be stored in a corresponding data entityfor retrieval. However, timeseries operators 906 can be configured towait until query time to process one or more timeseries that are readinfrequently to avoid performing unnecessary processing operations.

In some embodiments, timeseries operators 906 are triggered in aparticular sequence defined by a directed acyclic graph (DAG). The DAGmay define a workflow or sequence of operations or transformations toapply to one or more input timeseries. For example, the DAG for a rawdata timeseries may include a data cleansing operation, an aggregationoperation, and a summation operation (e.g., adding two raw datatimeseries to create a virtual point timeseries). The DAGs can be storedin a DAG database 930 within storage 614, or internally withintimeseries processing engine 904. DAGs can be retrieved by workflowmanager 922 and used to determine how and when to process incoming datasamples. Exemplary systems and methods for creating and using DAGs aredescribed in greater detail below.

Timeseries operators 906 can perform aggregations for dashboards,cleansing operations, logical operations for rules and fault detection,machine learning predictions or classifications, call out to externalservices, or any of a variety of other operations which can be appliedto timeseries data. The operations performed by timeseries operators 906are not limited to timeseries data. Timeseries operators 906 can alsooperate on event data or function as a billing engine for a consumptionor tariff-based billing system. Timeseries operators 906 are shown toinclude a sample aggregator 908, a virtual point calculator 910, aweather point calculator 912, a fault detector 914, and an eventseriesgenerator 915.

Still referring to FIG. 9, timeseries processing engine 904 is shown toinclude a DAG optimizer 918. DAG optimizer 918 can be configured tocombine multiple DAGs or multiple steps of a DAG to improve theefficiency of the operations performed by timeseries operators 906. Forexample, suppose that a DAG has one functional block which adds“Timeseries A” and “Timeseries B” to create “Timeseries C” (i.e., A+B=C)and another functional block which adds “Timeseries C” and “TimeseriesD” to create “Timeseries E” (i.e., C+D=E). DAG optimizer 918 can combinethese two functional blocks into a single functional block whichcomputes “Timeseries E” directly from “Timeseries A,” “Timeseries B,”and “Timeseries D” (i.e., E=A+B+D). Alternatively, both “Timeseries C”and “Timeseries E” can be computed in the same functional block toreduce the number of independent operations required to process the DAG.

In some embodiments, DAG optimizer 918 combines DAGs or steps of a DAGin response to a determination that multiple DAGs or steps of a DAG willuse similar or shared inputs (e.g., one or more of the same inputtimeseries). This allows the inputs to be retrieved and loaded oncerather than performing two separate operations that both load the sameinputs. In some embodiments, DAG optimizer 918 schedules timeseriesoperators 906 to nodes where data is resident in memory in order tofurther reduce the amount of data required to be loaded from thetimeseries database 928.

Timeseries processing engine 904 is shown to include a directed acyclicgraph (DAG) generator 920. DAG generator 920 can be configured togenerate one or more DAGs for each raw data timeseries. Each DAG maydefine a workflow or sequence of operations which can be performed bytimeseries operators 906 on the raw data timeseries. When new samples ofthe raw data timeseries are received, workflow manager 922 can retrievethe corresponding DAG and use the DAG to determine how the raw datatimeseries should be processed. In some embodiments, the DAGs aredeclarative views which represent the sequence of operations applied toeach raw data timeseries. The DAGs may be designed for timeseries ratherthan structured query language (SQL).

In some embodiments, DAGs apply over windows of time. For example, thetimeseries processing operations defined by a DAG may include a dataaggregation operation that aggregates a plurality of raw data sampleshaving timestamps within a given time window. The start time and endtime of the time window may be defined by the DAG and the timeseries towhich the DAG is applied. The DAG may define the duration of the timewindow over which the data aggregation operation will be performed. Forexample, the DAG may define the aggregation operation as an hourlyaggregation (i.e., to produce an hourly data rollup timeseries), a dailyaggregation (i.e., to produce a daily data rollup timeseries), a weeklyaggregation (i.e., to produce a weekly data rollup timeseries), or anyother aggregation duration. The position of the time window (e.g., aspecific day, a specific week, etc.) over which the aggregation isperformed may be defined by the timestamps of the data samples oftimeseries provided as an input to the DAG.

In operation, sample aggregator 908 can use the DAG to identify theduration of the time window (e.g., an hour, a day, a week, etc.) overwhich the data aggregation operation will be performed. Sampleaggregator 908 can use the timestamps of the data samples in thetimeseries provided as an input to the DAG to identify the location ofthe time window (i.e., the start time and the end time). Sampleaggregator 908 can set the start time and end time of the time windowsuch that the time window has the identified duration and includes thetimestamps of the data samples. In some embodiments, the time windowsare fixed, having predefined start times and end times (e.g., thebeginning and end of each hour, day, week, etc.). In other embodiments,the time windows may be sliding time windows, having start times and endtimes that depend on the timestamps of the data samples in the inputtimeseries.

FIG. 10 is an example entity graph of entity data according to anembodiment of the present disclosure. The example of FIG. 10 assumesthat an HVAC fault detection application has detected an abnormaltemperature measurement with respect to Temperature Sensor 1012.However, Temperature Sensor 1012 itself may be operating properly, butmay rely on various factors, conditions, and other systems and devicesto measure the temperature properly. Accordingly, for example, the HVACfault detection application may need to know the room 1014 in which theTemperature Sensor 1012 is located, the corresponding temperaturesetpoint, the status of the VAV 1004 that supplies conditioned air tothe room 1014, the status of the AHU 1002 that feeds the VAV 1004, thestatus of the vents in the HVAC zone 1010, etc., in order to pin pointthe cause of the abnormal measurement. Thus, the HVAC fault detectionapplication may require additional information from various relatedsubsystems and devices (e.g., entity objects), as well as the zones androoms (e.g., entity objects) that the subsystems and devices areconfigured to serve, to properly determine or infer the cause of theabnormal measurement.

Referring to FIG. 10, entity graph 1000 shows the relationship betweenTemperature Sensor 1012 and related entities via relational entities(e.g., feeds, hasPoint, hasPart, Controls, etc.). For example, entitygraph 1000 shows that Temperature Sensor 1012 provides temperaturereadings (e.g., hasPoint) to the VAV 1004 and the HVAC Zone 1010. An AHU1002 provides (e.g., feeds) the VAV 1004 with chilled and/or heated air.The AHU 1002 receives/provides power readings (e.g., hasPoint) from/to aPower Meter 1008. The VAV 1004 provides (e.g., feeds) air to HVAC Zone1010 using (e.g., hasPart) a Damper 1006. The HVAC Zone 1010 providesthe air to Room 1014. Further, Rooms 1014 and 1020 are located in (e.g.,hasPart) Lighting Zone 1018, which is controlled (e.g., controls) byLighting Controller 1016.

Accordingly, in the example of FIG. 10, in response to receiving thefaulty measurement from Temperature Sensor 1012, the HVAC faultdetection application and/or analytics service 624 can determine fromthe entity graph that the fault could be caused by some malfunction inone or more of the other related entities, and not necessarily amalfunction of the Temperature Sensor 1012. Thus, the HVAC faultdetection application and/or the analytics service 624 can furtherinvestigate into the other related entities to determine or infer themost likely cause of the fault.

Space Graph

Referring generally to FIGS. 11-21, systems and methods for creating,managing, and utilizing space graphs for a building management systemare shown and described, according to various exemplary embodiments. Thelimitations of building management systems to handle multivariaterelationships between spaces, assets, and/or people can be addressed bycreating space graphs. In some embodiments, a space graph is a type ofknowledge graph (e.g., graph data structure) where each node of thespace graph represents an entity and each edge is directed (e.g., from afirst node to a second node) and represents a relationship betweenentities (e.g., indicates that the entity represented by the first nodehas a particular relationship with the entity represented by the secondnode).

Entities can be things and/or concepts related to spaces, people, and/orasset. For example, the entities could be “B7F4 North”, “Air HandlingUnit,” and/or “meeting room.” The nodes can represent nouns while theedges can represent verbs. For example, the edges can be “isA,”“hasPart,” and/or “feeds”. While the nodes represent the building andits components, the edges describe how the building operations, bothtogether create a digital twin of a particular building. In someembodiments, the entities include properties or attributes describingthe entities (e.g., a thermostat may have a particular model numberattribute). The components of the space graph form large networks thatencode semantic information for a building.

The space graph is configured to enable flexible data modeling foradvanced analytics, control, and/or artificial intelligenceapplications, in some embodiments. These applications may require, orbenefit from information modeling including interconnected entities.Other data modeling techniques based on a table, a hierarchy, adocument, and/or a relational database may not be applicable. The spacegraph can be a foundational knowledge management layer to support otherhigher level applications, which can be, complex root cause, impactanalysis, building powerful recommendation engines, product taxonomyinformation services, etc. Such a multilayer system, a system of systemtopologies, can benefit from an underlying space graph.

The space graph can be a data contextualization layer for alltraditional and/or artificial intelligence applications. The space graphcan be configured to capture evidence that can be used to attribute thestrengths of entity relationships within the space graph, providing theapplications which utilize the space graph with context of the systemsthey are operating. Without context (e.g., who the user is, what theuser is looking for, what the target of a user request is, e.g., find ameeting room, increase a temperature in my office) these applicationsmay never reach their full potential. Furthermore, the space graphprovides a native data structure for constructing question and answertype systems, e.g., a chatbot, that can leverage and understand intent.

The space graph may not be a configuration database but may be a dynamicrepresentation of a space. The space graph can include operational datafrom entities which it represents, e.g., sensors, actuators, card accesssystems, occupancy of a particular space, thermodynamics of the space asa result of actuation, etc. The space graph can be configured tocontinually, and/or periodically, ingest new data of the space and thusthe space graph can represent a near real-time status of cyber-physicalentities and their inter-relationships. For this reason, artificialintelligence can be configured to introduce a virtual entity and newsemantic relationships among entities, in some embodiments.

The space graph is configured to facilitate adaptive controls, in someembodiments. The space graph can be configured to adapt and learn overtime. The space graph can be configured to enable dynamic relationshipsbetween building information and other facility and enterprise systemsto create new insights and drive new optimization capabilities forartificial intelligence systems. As relationships can be learned overtime for the space graph, the artificial intelligence systems and alsolearn overtime based on the space graph.

Referring now to FIG. 11, the cloud entity service 626 is shown ingreater detail including a space graph database 1120, according to anexemplary embodiment. The cloud entity service 626 includes the spacegraph database 1120, a space graph learning service 1104, a dataingestion service 1114, an agent service 1116, and a query system 1118.The space graph database can be a knowledge base that represents howentities are related to each other at a given point in time. The spacegraph database 1120 can extend the utility and effectiveness of digitaltwins and/or single and/or multi-agent systems. The space graph database1120 can correspond to an entity (e.g., a building) and/or a subset ofentities (building equipment and spaces within the building) withreal-time status synchronization of information, relationships, and/orentities of the building.

The space graph database 1120 includes nodes 1122-1130. The nodes1122-1130 represent physical entities and/or software components. Entity1124 could be a user, a space, a piece of software, equipment, and/orany other type of entity. The other nodes 1122 and 1126-1130 representother entities. Agent 1126 is a software agent configured to implementartificial intelligence, in some embodiments. In some embodiments,control algorithm 1128 represents a control algorithm that can begenerated by the agent 1126. Entity type 1130 represents a typedefinition for the entity 1124 (for example, if the entity 1124represents “Thermostat A” the entity type 1130 may be a set entity type,“Thermostat” which defines the type of the entity 1124 for the spacegraph database 1120). Information 1122 can be collected data associatedwith the entity 1124 (for example, if the entity 1124 is a thermostat,the information 1122 could be temperature measurements of thethermostat, a fault log of the thermostat, etc.).

The space graph database 1120 includes directional relationships, edges1132-1144. The edges 1132-1144 can define relationships between theentities of the nodes. Each of the entities of the space graph database1120 can include one or multiple different edges between each other. Forexample, entity 1124 has an edge 1140 from the entity 1124 to the agent1126 defining the relationship between the entity 1124 and the agent1126. Furthermore, the edge 1142 between the agent 1126 and the entity1124 represents another, different relationship between the agent 1126and the entity 1124. For example, the edge 1140 may indicate that theentity 1124 is associated with the agent 1126 while the edge 1142 mayindicate that the agent 1126 generates control information forcontrolling the entity 1124.

The data ingestion service 1114 of the cloud entity service 626 isconfigured, in some embodiments, to ingest data into the space graphdatabase 1120. The data ingestion service 1114 is configured, in someembodiments, to receive building information from building data sources1102 and ingest the data into the space graph database 1120. Thebuilding data sources 1102 may be data sources configured to collectinformation for the entities of the space graph database 1120. Forexample, the building data sources 1102, are, in some embodiments,actual building equipment, a BMS, sensors, actuators, etc. For example,the building data sources 1102 can be the devices of the school 110, thehospital 120, the factory 30, and/or the office 140 as described withreference to FIG. 1.

Furthermore, the data sources 1102 can be any of the equipment and/orsystems as described with reference to FIGS. 2-5. Furthermore, externalsystems that gather information (e.g., social media data, cellphonedata, equipment specifications, building information, weatherinformation, etc.) can be included in the building data sources 1102.For example, the weather service 152, the news service 154, the documentservice 156, and/or the media service 158 can be included within thebuilding data sources 1102 and can provide building data to the dataingestion service 1114.

The data ingestion service 1114 is configured to ingest buildinginformation received from the building data sources 1102 into the spacegraph database 1120 in some embodiments. For example, the data ingestionservice 1114 can be configured to identify, based on the building data,values, measurements, and/or any other information of the building datathat is directed associated with an entity of the space graph database1120. In this regard, the data ingestion service 1114 can cause thebuilding data associated with the entity to be stored within the spacegraph database 1120 with an association to the identified entity.

For example, if the building data includes collected data for the entity1124, the data ingestion service 1114 can cause information 1122, whichrepresents information of the entity 1124, to include the collecteddata. In some embodiments, the node entity 1124 itself includes its owninformation storage instead of a separate node (e.g., the information1122). In this regard, the data ingestion service 1114 can cause theentity 1124 to store the collected data. The data ingestion service 1114is configured to perform similar data ingestion operations for one orall of the entities of the space graph database 1120, in someembodiments. The data ingestion service 1114 can be configured to ingestthe data as the data becomes available and/or at a time period.

In some embodiments, the space graph learning service 1104 is configuredto generate and/or update the entities (e.g., the nodes 1122-1130)and/or the edges 1132-1142. The space graph learning service 1104 can beconfigured to receive the building data from the building data sources1102 and generate and/or manage the space graph database 1120 based onthe received building data. In some embodiments, the space graphlearning service 1104 generates the entire space graph database 1120 toinstantiate a space graph for a particular building. For example, datarepresenting particular entities and/or their relationships can bewithin the building data.

Based on the building data, the space graph learning service 1104 isconfigured to generate the entire space graph database 1120. One exampleof such a generation step can be the data point string analysismechanisms as described in U.S. patent application Ser. No. 15/207,376filed Jul. 11, 2016, the entirety of which is incorporated by referenceherein. Furthermore, another example of generating the relationshipsbetween entities can be the equipment relationship derivation mechanismsfor determining a relationship between two pieces of equipment asdescribed in U.S. patent application Ser. No. 15/400,926 filed Jan. 6,2017, the entirety of which is incorporated by reference herein.

The space graph learning service 1104 includes a new relationshipidentifier 1106. The new relationship identifier 1106 is configured togenerate relationships between entities that do not exist in the spacegraph database 1120. The new relationship identifier 1106 is configured,in some embodiments, to generate relationships for nodes uponconstruction of the space graph database 1120 and/or after the spacegraph database 1120 has been constructed to reflect relationshipsderived from new data indicating changes which may have been made to thebuilding represented by the space graph database 1120 and/or newentities for the space graph database 1120. The new relationshipidentifier 1106 can analyze trends associated with data of the entitiesof the space graph database 1120. For example, ingested data into thespace graph database 1120 may indicate that an access control systemregisters access and immediately after, a room becomes occupied. The newrelationship identifier 1106 can derive a relationship between theaccess control system and the room, i.e., that the access control systemcontrols access to the room. Various supervised or unsupervised learningmechanisms (e.g., neural networks, Bayesian networks, etc.) can beutilized to analyze data ingested into the space graph database 1120and/or received directly from the building data sources 1102 to identifythe relationships.

In some embodiments, the new relationship identifier 1106 continuallyand/or periodically identifies new relationships for the space graphdatabase 1120. In some embodiments, the new relationship identifier 1106is triggered by the data ingestion service 1114 to identify newrelationship in response to the data ingestion service 1114 ingestingnew data into the space graph database 1120. In some embodiments, thedata utilized by the new relationship identifier 1106 to identify thenew relationships is the entire space graph database 1120, e.g., thenodes and/or the edges between the nodes of the space graph database1120.

The space graph learning service 1104 includes a temporary relationshipadder 1108. Based on new relationships identified by the newrelationship identifier 1106, the temporary relationship adder 1108 cancause the space graph database 1120 to be updated with a temporaryrelationship. The temporary relationship adder 1108 is configured, insome embodiments, to cause an edge between a first nod representing afirst entity and a second node representing a second entity to be addedto the space graph database 1120. The temporary relationship mayindicate that a relationship between two entities may exist. Thetemporary relationship can cause other system that rely on the spacegraph database 1120 to adjust their operations, assuming based on thetemporary relationship. As additional data is collected, the temporaryrelationship can be verified as existing (which can be based updates tocontrol algorithms used to control equipment) and replaced with apermanent relationship via the relationship adder 1110.

The relationship adder 1110 can be configured to add a relationshipbetween entities in the space graph database 1120. In some embodiments,in response to the new relationship identifier 1106 identifying arelationship existing between a first entity and a second entity, therelationship adder 1110 can be configured to add the relationship to thespace graph database 1120. In some embodiments, the relationship adder1110 adds the relationship by causing an edge to exist between the firstentity and the second entity of the space graph database 1120. In someembodiments, the relationship adder 1110 can assign a particular type tothe edge, “isAPartOf,” “controls,” “hasA,” etc. In some embodiments, therelationship adder 1110 is configured to derive the type of therelationship from the ingested building data of the space graph database1120. In some embodiments, the relationship adder 1110 is configured toderive the type of the relationship from the types of the entities whichthe relationship is between.

The space graph learning service 1104 includes a control algorithmrelationship adder 1112. The control algorithm relationship adder 1112is configured to identify an impact relationship cause by the executionof a control algorithm and cause the space graph database 1120 to storethe impact relationship. In some embodiments, the control algorithmrelationship adder 1112 identifiers an indirect relationship betweenentities of the space graph database 1120. For example, a VAV unitentity may operate temperature of a zone. A control algorithm can causethe VAV unit to operate in a particular manner to regulate thetemperature of the zone. However, execution of the algorithm to regulatethe temperature of the zone can indirectly impact other entities.

For example, a neighboring zone next to the zone can have itstemperature affected by the execution of the control algorithm. Thistype of relationship may not be direct or physical but may be anindirect impact resulting from execution of a control algorithm. Forthis reason, the control algorithm relationship adder 1112 can identifyrelationships between entities of the space graph database 1120 whichare the result of the execution of the control algorithm and cause thespace graph database 1120 to store a new edge representing the indirectrelationship to the space graph database 1120.

In some embodiments, the control algorithm causing the indirect impact,or other control algorithms, can be adjusted. For example, if the agent1126 generates a control algorithm for a first zone and operation of thecontrol algorithm affects temperature in the first zone but alsoindirectly in a second zone, another agent associated with the secondzone can coordinate with the agent 1126 to combine control algorithmsfor the first and second zones such that control of the first zone isconsidered when controlling the second zone. This cooperation betweenagents can be implemented based on the presence of an indirectrelationship caused by a control algorithm.

The query system 1118 can be a system utilized to process queries of thespace graph database 1120. The queries can be generated by clientdevices 548 via an chatbot interface application 1100. The chatbot 1100can be configured to receive queries in text and/or spoken word from aninput device (e.g., display screen, keyboard, microphone, etc.) andprovide the query to the query system 1118. The queries may be in theform of a database query and/or may be in the form of text. For example,the queries can be queries similar to the BRICK queries described inU.S. Provisional Patent Application No. 62/751,926 filed Oct. 29, 2018,the entirety of which is incorporated by reference herein.

In some embodiments, the query system 1118 integrates with the chatbot1100 configured to converse with a user of the client devices 548. Thechatbot 1100 can be similar to a CORTANA and/or SIRI system. The chatbot1100 can be configured to perform natural language processing on spokenquestions regarding information of the space graph database 1120,resolve the question via the NLP into a semantic query for the spacegraph database 1120, and query the space graph database 1120 for theinformation requested by the user of the client devices 548.

In some embodiments, a query may be “What temperature is this room?”Based on a location of the client devices 548, the query system 1118 candetermine what room the user is asking for information about, whatinformation the user is asking for, temperature, and what semantic querywould pull the temperature for the identified room from the space graphdatabase 1120. The query system 1118 can perform the generated query toretrieve the request information, and provide the information to theuser of the client device 548 via text and/or audio (e.g., via a screenor a speaker). The query can be based on the relationship and entitynames of the space graph database 1120 and, based on the query, a systemcan traverse the various nodes and relationships of the space graphdatabase 1120 to retrieve the information requested by the query whichcan be any type of information (e.g., measurements, relationshipsbetween equipment, etc.). In some embodiments, since operational data,relationships, and entities are stored by the space graph database 1120,no other relationships or nodes of other data structures besides thespace graph database 1120 need to be traversed to retrieve informationfor a query. The chatbot 1100 can be similar to and/or the same as theassistant described with reference to U.S. patent application Ser. No.16/028,126 filed Jul. 5, 2018 and U.S. patent application Ser. No.16/246,391 filed Jan. 11, 2019, the entity of both of which areincorporated by reference herein. In some embodiments, any system orsoftware component (e.g., the space graph learning service 1104, theagent 1126, the agent service 1116) can interact with the space graphdatabase 1120 by receiving information for the space graph database 1120based on a query. In this regard, the systems and/or components caninclude some and/or all of the operations and/or components of the querysystem 1118 and/or can rely on the query system 1118 for performingqueries.

The agent service 1116 is configured, in some embodiments, to manageagents of the space graph database 1120. For example, the agent service1116 is configured, in some embodiments, to instantiate agents withinthe space graph database 1120 to exist as nodes within the space graphdatabase 1120 and/or in some embodiments, is configured to operate theagents of the space graph database 1120. The agent service can beconfigured, in some embodiments, to execute all of the agents of thespace graph database 1120 external to the space graph database 1120 andinject the results of the operation of the agents into the space graphdatabase 1120. However, in some embodiments, the agents of the spacegraph database 1120 within the space graph database 1120 itself. Theagent service 1116 and/or the agents of the space graph database 1120can implement the agent based operations as described with reference toU.S. patent application Ser. No. 15/586,104 filed May 3, 2017, U.S.patent application Ser. No. 15/367,167 (now U.S. Pat. No. 9,817,383)filed Dec. 1, 2016, U.S. patent application Ser. No. 15/723,624 filedOct. 3, 2017, U.S. patent application Ser. No. 15/968,278 filed May 1,2018, U.S. patent application Ser. No. 16/036,685 filed Jul. 16, 2018,U.S. patent application Ser. No. 16/008,885 filed Jun. 14, 2018, theentirety of each of these patent application is incorporated byreference herein.

The agent 1126 can be a software component. In some embodiments, theagent 1126 is a long-running small process that classifies the status ofentities of the space graph database 1120 into a pre-determined category(e.g., a room can be classified into occupied, unoccupied, stand-by,in-meeting, etc.). In some embodiments, the classification is performedwith various classification rules and/or machine learning models (e.g.,neural networks, Bayesian models, etc.). The data used in theclassification can be the relationships, entities, and/or ingested dataof the space graph database 1120. In some embodiments, the agent 1126 isconfigured to interact with other agents of the space graph database1120 and utilize a space graph to answer queries or update entities. Forexample, a comfort agent is configured to retrieve occupancy history ofa user via the space graph database 1120 and retrieve outdoor weatherinformation from the space graph database 1120 to generate a temperaturecontrol strategy (e.g., an optimized temperature control strategy), andthen make the control strategy available as a comfort schedule.

In some embodiments, the agent 1126 is a space agent representing aspace. In some embodiments, the space agent is a zone agent, a roomagent, a floor agent, a building agent, etc. The space agent isconfigured to monitor health of the equipment that serve the space ofthe space agent, in some embodiments. Furthermore, the space agent isconfigured to manage setpoints for the space and calculate an effectivesetpoint for the space.

In some embodiments, the agent 1126 is a control agent which can begenerated by the agent service 1116 to perform control operations. Insome embodiments, the control agent is a global data sharing agentconfigured to publish information to other agents within the space graphdatabase 1120 or other service outside the space graph database 1120, atemporary occupancy override agent configured to determine whether tooverride an occupancy state of a space, a scheduled exception agentconfigured to determine an exception to a control schedule, a flowsetpoint reset agent configured to reset a flow setpoint, an optimalstart and stop agent configured to determine times at which to start orstop equipment, a reheat valve control agent configured to generatecontrol signals for a reheat valve, an unoccupied mode night setbackagent configured to set other entities to unoccupied based on time ofday, and a chiller sequencing agent configured to sequence an agent, insome embodiments.

Referring now to FIG. 12, the applications 630 as described withreference to FIG. 6 is shown in greater detail, according to anexemplary embodiment. The applications 630 can be configured to performoperations based on the information of the space graph database 1120 tooperate the building subsystems 528. For example, the applications 630can be configured to query the space graph database 1120 forinformation, make adjustments to information in the space graph database1120, and/or make perform control actions based on the adjustments tothe space graph database 1120. In some embodiments, the agents 1202-1214are included within the space graph database 1120 and query the spacegraph database 1120 for information to perform control actions.

In some embodiments, the applications 630 are occupant basedapplications configured to perform local environmental control (e.g.,control of temperature and/or lighting), perform desk booking, performroom booking and/or meeting management, perform contact informationmanagement and/or contact status management, perform wayfinding, performemployee mass notifications, perform attendance and activity tracking,perform enhanced local environmental control, advanced desk booking,efficient meeting management, contact management, advanced wayfinding,mass notifications for employees and visitors, visitor management andregistration, patient room monitoring and control, local desk controland adjustments, hot desk booking, advanced meeting management andbooking, hot desking features, asset tracking and advanced wayfinding,enhanced mass notifications, nurse call features and automated code bluefeatures, issue reporting, and/or any other occupant related feature.

In some embodiments, the applications 630 perform tenant applicationsconfigured to generate interfaces allowing a tenant to view energyusage. Furthermore, the applications 630 may allow the tenant to makepayments in an amount for their energy usage. The applications 630 canrecord energy usage via the space graph database 1120 and various tenantentities can be related to the energy usage. Bills can be generated bythe applications 630 and added to the space graph database 1120 andlinked to the particular tenants. Therefore, in response to a request bya tenant to view their bill, the applications 630 can retrieve thetenant energy usage information tied to that user and the bill tied tothat user and cause the information to be displayed.

In some embodiments, the applications 630 perform facility managementfor an owner of a building. The owner can view information pertaining totenant energy usage, building spending and energy consumption, publicawareness data, heat maps of spaces generated based on occupancy levels,fault detection and diagnostics (FDD) with monetization, fault trendsand/or work order conversations, energy intensity by tenant and buildingload, utility bill data, etc.

In some embodiments, the applications 630 are configured to performbuilding owner features e.g., automated tenant billing, generation ofspace level key performance indications (KPIs) and/or associatedanalytics, analysis of sustainability statistics and tracking, securityalarm management, equipment failure analytics, awareness between BMSsystems, file integrity monitoring (FIM) and tracking, etc.

The applications 630 include a meeting room agent 1202. The meeting roomagent 1202 is configured to allow building staff to book a meeting roomvia MICROSOFT OUTLOOK and/or Cortana. The meeting room agent 1202 isconfigured, in some embodiments, to identify with an artificialintelligence, if a very important person (VIP) will be a meetingattendee. Based on the attendees and/or whether the meeting attendeesinclude a VIP person, the meeting room agent 1202 is configured toselect a meeting room and/or adjust other scheduled meetings and/ormeeting rooms, to properly accommodate the VIP person in a meeting roomwith a particular quality level. The meeting room agent 1202 isconfigured to interact with an arrival and departure agent 1204 toperform the VIP meeting room booking.

In some embodiments, the artificial intelligence of the meeting roomagent 1202 is configured to generate shortlists for all meeting rooms ofa building based on the size and available number of attendees of eachmeeting room. In some embodiments, the size and available number ofattendees for each meeting room is received from a workplace designagent 1210. The meeting room agent 1202 is configured to utilize theshortlists in booking meetings. Furthermore, when booking meeting rooms,the room agent 1202 can generate requests that additional chairs and/ortables be added to the booked room to accommodate the number ofattendees in the request. The request may get provided to a facilitymanager via a facility operations agent 1208.

In some embodiments, the meeting room agent 1202 is configured toimplement instant and/or immediate booking. The artificial intelligenceof the meeting room agent 1202 is configured to find a nearest availablemeeting room for an immediate booking and, if necessary, can re-adjustother meeting room bookings to accommodate the immediate booking. Toidentify the nearest meeting room, the meeting room agent 1202 can beconfigured to communicate with the workplace design agent 1210.

In some embodiments, the meeting room agent 1202 is configured to detectwhen an employee enters a meeting room unexpectedly. The artificialintelligence of the meeting room agent 1202 can cause audio to playwithin the room welcoming the employee by naming and asking if the userwould like to book the room and/or for how long the user would like theroom to be booked. Based on other bookings for the same room, themeeting room agent 1202 is configured to grant and/or deny the bookingrequest.

In some embodiments, when a user books a meeting room, the artificialintelligence of the meeting room agent 1202 is configured to analyze theattendees and the hosts with the meeting location, subject, agenda,and/or duration. The artificial intelligence of the meeting room agent1202 is configured to limit effective meetings to be between apredefined length of time (e.g., 30-45 minutes) and workshops to bewithin another predefined length of time (e.g., 60 minutes). The meetingbooking analysis and limits can be performed by the meeting room agent1202 and/or by the workplace design agent 1210.

In some cases, a meeting attendee (whether employee or visitor) may berunning late to a meeting scheduled by the meeting room agent 1202. Insuch a case, the attendee can utilize the chatbot 1100 to communicatewith and inform the meeting room agent 1202 that the attendee is runninglate. The meeting room agent 1202 can then be configured to notify theorganizer and ask the organizer what he wishes to do. The meeting roomagent 1202 is configured to notify the other attendees and if necessary,is configured to adjust the meeting schedule accordingly, in someembodiment. In some embodiments, the meeting room agent 1202 isconfigured to cause smart screens of the meeting to automaticallydisplay a notification that the late attendee is running late.Furthermore, upon arrival of late attendee, the meeting organizer may beautomatically notified via the chatbot 1100. The meeting room agent 1202is configured, in some embodiments, to communicate with the parkingagent 1206, a lobby agent, the arrival and departure agent to identifywhether an attendee is late to a meeting.

In some embodiments, the meeting room agent 1202 is configured todetermine whether no one has showed up for a scheduled meeting. Inresponse to no one showing up for a scheduled meeting, the meeting roomagent 1202 is configured to ask an organizer, via the chatbot 1100, ifthe meet room is still required by the organizer. In some embodiments,the meeting room agent 1202 is configured to keep the meeting booked orcancel the meeting based on a response provided by the host via thechatbot 1100. Furthermore, the meeting room agent 1202 is configured toask, via the chatbot 1100 whether the host needs to extend the bookingand can be configured to extend the meeting time, select a new meetingroom for the meeting, and/or adjust other scheduled meetings for theroom to accommodate the extended meeting time. If the organizer does notanswer the questions of the meeting room agent 1202 within a predefinedamount of time, the meeting room agent 1202 is configured to cancel themeeting, in some embodiments. The meeting room agent 1202 is configured,in some embodiments, to coordinate to the workplace design agent 1210 tofacilitate management of meeting rooms when attendees do not show up ata scheduled time.

In some embodiments, if one or more guests are waiting in the meetingroom alone and the organizer does not show up to the meeting, themeeting room agent 1202 is configured to automatically notify theorganizer of their absence and provide an alert to a receptionist totake appropriate actions. If the host does not reply to the notificationof the meeting room agent 1202, the meeting room agent 1202 isconfigured to cancel the meeting. If the organizer indicates to delaythe meeting, the meeting room agent 1202 can provide a notification tothe meeting attendees of the delay. The meeting will be either becanceled if the host does not respond, or will be delayed according tohis response. For a situation where an organizer who does not show upfor a scheduled meeting, the meeting room agent 1202 can be configuredto coordinate with the workplace design service 1210 to take appropriateactions.

In some embodiments, before a meeting starts, the meeting room agent1202 is configured to automatically adjust meeting room temperaturesettings according to meeting requirements and participants preferences.During the meeting, using chatbot 1100, the participants can be able tocontrol all features in the room, temperature, lighting, etc. bycommunicating with the meeting room agent 1202. The automatic and manualcontrol of the meeting room can be performed by the meeting room agent1202 with the facility operations agent 1208.

In some embodiments, the meeting room agent 1202 is configured toidentify meeting room attendees via facial recognition and is configuredto cause a speaker of the room to play a greeting for each attendeegreeting the attendee by name. The meeting room agent 1202 is configuredto automatically login an organizer of the meeting to a collaborationscreen to enable collaboration through annotation, screen sharing, andconferencing. The identification, greeting, and smart screen featurescan be performed via the meeting room agent 1202 with the workplacedesign agent 1210.

The meeting room agent 1202 is configured to automatically identify ifvideo or voice conferencing is required for a meeting. As soon as theorganizer enters the meeting room, the meeting room agent is configuredto cause speakers of the room to great the organizer and automaticallylink the organizer to a conference call if the meeting is booked with aconference call. The conference call implementation can be performed bythe meeting room agent 1202 and/or the workplace design agent 1210.Furthermore, the meeting room agent 1202 is configured to cause laptops,notebooks, and/or smartphones to be connected to the meeting roomequipment via their network connection to a wireless network instead ofrequiring specialized cables.

In some embodiments, the meeting room agent 1202 is configured to askthe meeting organizer via the chatbot 1100 whether the organizer wouldlike the meeting to be recorded via meeting video cameras, via meetingroom microphones, via speech-to-text transcription services, and/or viaan action assignment service. The meeting room agent 1202 is configuredto cause the recorded meeting information to be stored a drive (e.g., anetwork drive). In some embodiments, via email, an, application, and/ortext messaging, the meeting room agent 1202 is configured to follow upwith attendees and the organizer to provide reminders according to theaction assignment determined during the meeting. Furthermore, via thechatbot 1100, the meeting room agent 1202 is configured to ask theorganizer fi the organizer would like the meeting recording to beprovided to the other meeting attendees of the meeting. The meeting roomrecording features can be implemented by the meeting room agent 1202and/or the workplace design agent 1210.

In some embodiments, as the end of meeting approaches, the meeting roomagent 1202 is configured to ask the organizer, via chatbot 110, if theorganizer requires additional time for the meeting. If additional timeis required than originally booked, the meeting room agent 1202 isconfigured to check if there is another consecutive meeting in the sameroom. If there is a consecutive meeting, the meeting room agent 1202 isconfigured to check the availability of other meeting rooms and will actaccordingly to either re-locate the current meeting or relocate theconsecutive meeting. The meeting room agent 1202 is configured to notifythe relocated meeting attendees with the new changes via the chatbot1100. The meeting room extension features can be performed by themeeting room agent 1202 and/or the workplace design agent 1210.

In some embodiments, at the end of the meeting, the meeting room agent1202 is configured to cause speakers of the room to play a goodbyemessage and a reminder for them to not forget their personal belongings.The meeting room agent 1202, via a camera of the room, is configured todetermine whether all attendees have left the room and/or whether anypersonal belongings are left behind in the room. The meeting room agent1202 is configured to provide a notification to all users via thechatbot 1100 and/or via text message that an item has been left behind.If the users and/or organizer do not respond within a predefined amountof time from sending out the notifications (e.g., one minute), themeeting room agent 1202 is configured to notify a front desk operatorand/or security personal. The left behind items operations can beperformed by the meeting room agent 1202 and/or by the facilityoperations agent 1208 and/or the arrival and departure agent 1204.

In some embodiments, the meeting room agent 1202 facilitates ordering offood and/or beverages via the chatbot 1100. The meeting room agent 1202is configured to store food and/or beverage preferences for eachattendee and prompt the attendees in future meetings if they would liketo order the same food and/or beverages ordered at a previous meetingvia the chatbot 1100. The meeting room agent 1202 is configured toprovide a notification to a catering service (e.g., the food andbeverage agent 1212) to facilitate the ordering of the food and/orbeverages determined by the meeting room agent 1202 via the chatbot1100. In some embodiments, the meeting room agent 1202 is configured todetermine when the food and/or beverages ordered are delivered. If thefood and/or beverages are not delivered within a predefined amount oftime, the catering service does not acknowledge the orders, and/or theorders arrive after a predefined amount of time, the meeting room agent1202 can provide a receptionist with a notification and an indicationthat action should be taken to correct the poor service in the future.

In some embodiments, the meeting room agent 1202, together with theworkplace design agent 1210 and/or the facility operations agent 1208,is configured to operate a display in a kitchen, the digital displaythat shows meeting room occupancies and attendees with their respectiveorders. Once a meeting is over the, kitchen can be requested by themeeting room agent 1202 via the digital display to clean up and set theroom for the next meeting. The meeting room agent 1202 is configured, insome embodiments, to use video streams of a camera of the room andobject recognition to identify if the meeting room is cleaned/arrangedor not, and will provide a notification to cleaning personnel if theroom is not timely cleaned.

In some embodiments, an organizer can utilize a smart meeting roomcollaboration screen of the meeting room operated by the meeting roomagent to enable collaboration through annotation, screen sharing, andconferencing. The meeting room screen may allow one touch dial-in toconference features and/or automated video conferencing start.Furthermore, during a meeting, and organizer and/or employee caninstantly share documents, screen, group chatting, group editing ofdocuments, via the meeting room agent 1202, to the collaboration screen.

In some embodiments, before or during the meeting, attendees can requestvia the chatbot 1100 any supplies, equipment, and/or stationary. Themeeting room agent 1202 is configured to receive the request and providea notification to a facility manager with the request, e.g., provide anotification to the facility operations agent 1208. The request willnotify the facility manager with the request made by the meetingattendees. If the facility team does not address the request within apredefined amount of time (e.g., one minute), the meeting room agent1202 and/or the facility operations agent 1208 is configured to providea reminder to the facility team.

In some embodiments, attendees can report any malfunction or breakdownof any of the equipment in the meeting room via the chatbot 1100 to themeeting room agent 1202. Some equipment failure can be automaticallydetected through the meeting room agent 1202. The meeting room agent1202 can be configured to notify service, information technology, and/orfacility management using the chatbot 1100 to address the issue. In someembodiments, the notification is delivered to the appropriate servicepersonal via the facility operations agent 1208.

In some embodiments, the arrival and departure agent 1204 is configuredto, upon entrance to a lobby, identify visitors via facial recognition.Information regarding a history of the visitors can be displayed to areceptionist by the arrival and departure agent 1204 based on theidentification of the user. Furthermore, for unregistered visitors, thearrival and departure agent 1204 is configure dot identify unregisteredvisitors through social media profile pictures, e.g., LINKEDIN,FACEBOOK, etc.

In some embodiments, if a visitor is late for a meeting, the arrival anddeparture agent 1204 is configured to text the visitor and ask thevisitor how long it will take him to make the meeting and/or whether ornot the visitor wishes to cancel the meeting. The arrival and departureagent 1204 is configured to communicate with the meeting room agent 1202to cancel and/or delay the meeting based on the input of the user.

In some embodiments, a mobile robot is operated by the arrival anddeparture agent 1204 to greet a visitor if the arrival and departureagent 1204 identifies the visitor via facial recognition when thevisitor arrives. In some embodiments, the robot escorts the visitor to ameeting room indicated by the arrival and departure agent 1204. In someembodiments, when the visitor arrives, the arrival and departure agent1204 determines whether an associated room is booked for the visitor. Ifno room is booked for the user, the arrival and departure agent 1204 isconfigured to provide an indication of no booking lobby personnel andthat the lobby personnel ask that the visitor wait in the lobby.

In some embodiments, the arrival and departure agent 1204 can determinewhether or not the host is within the building that the visitor arrivesat. In such a case, a human receptionist and/or the robot can beprompted by the arrival and departure agent 1204 to greet the visitorand will seat him in the lobby while the arrival and departure agent1204 contacts the host indicating that their visitor has arrived. If thehost does not confirm the attendance of the visitor within a predefinedamount of time, the arrival and departure agent 1204 is configured torequest that the receptionist take necessary actions.

In some embodiments, if the robot (e.g., via a video camera of therobot) and/or the arrival and departure agent 1204 is unable to identifythe visitor, the robot can be configured to ask him to the visitor toprovide a registration code associated with the visitor registrationcode so the robot associate the face biometrics with the registrationdetails. In some embodiments, if the visitor is not registered, therobot will ask him to proceed to the receptionist to register. In someembodiments, if the visitor is not attending a scheduled meeting, thearrival and departure agent 1204 is configured to prompt the host tocome can collect the visitor, in some embodiments.

In some embodiments, unregistered visitors will register at thereception of the building. Registration can be facilitated by thearrival and departure agent 1204 using face recognition to allow futurerecognition of the visitor. Furthermore, the car number plate and otherrelevant information will be automaticity coupled by the arrival anddeparture agent 1204 to the visitor for frictionless access controlsystem use in the future, in some embodiments.

In some embodiments, waiting visitors in a lobby of the building will beasked by the receptionist or a lobby robot if they require anybeverages. In response to the robot or receptionist receiving and/orinputting the request to the arrival and departure agent 1204, thearrival and departure agent 1204 is configured to operate with the foodand beverage agent 1212 to facilitate the order and provide an ordernotification to a kitchen. The arrival and departure agent 1204 isconfigured, in some embodiments, to remember requests of visitors andrecord the preferences of the visitors to a profile of the visitor. Infuture visits, the receptionist or lobby robot after identifying thevisitor via the arrival and departure agent 1204, can be able todirectly ask the visitor if he/she want to have their favorite beverage.

In some embodiments, the arrival and departure agent 1204 is configuredto provide visitors with a display of the latest environmental andenergy efficiency data of the building on lobby displays in the lobby.Furthermore, on the same lobby screens, the arrival and departure agent1204 is configured to display a special welcoming to very importantperson (VIP) in response to detecting the arrival at the lobby of theVIP.

In some embodiments, an employee can host visitors to a building thoughvisitor registration of a web-portal. The arrival and departure agent1204 is configured to send a text message and/or email message with alink to a one-time web page that has a registration form for registeringthe visitor. The visitor can register his/her information and vehicleinformation that can be recorded by the arrival and departure agent 1204will be used in frictionless visitor access systems.

In some embodiments, the arrival and departure agent 1204 is configuredto issue a visitor a virtual visitor ticket. The ticket will be in aformat that allows the visitor to save it in his smart phone built-indigital wallet. When required, the visitor will be able to use thevirtual ticket during his visit with his smart phone to access doorsthat require his identification. Furthermore, the arrival and departureagent 1204 is configured to provide the visitor with a journey map toget to a particular building and meeting room within the particularbuilding that the visitor is scheduled to attend a meeting. While thevisitor is in the building, the host and the security will be able tosee track their guest location in the building to verify that thevisitor is staying within predefined areas of the building.

For unregistered visitors, the arrival and departure agent 1204 canprompt, via the robot and/or a lobby display kiosk, that the visitordeclare their host. In response to the host being declared, the host isprovided with a notification indicating the arrival of the visitor via amobile application of a user device of the host. If the visitor does nothave an appointment with the host, the host can accept and/or reject theuser via the application. If the host accepts the unregistered visitors,they will register at the reception desk and will be given a virtualvisitor ticket via the arrival and departure agent 1204, in someembodiments. Some visitors will have a time limit for their presencewithin the building. If that limit is exceeded the arrival and departureagent 1204 is configured to notify the visitor, the host, and/orsecurity, in some embodiments.

In some embodiments, the arrival and departure agent 1204 is configuredto integrate with the meeting room booking system and constantly checkif the visitors have been registered prior to the meeting. Before themeeting, the arrival and departure agent 1204 is configured to remindnon-registered visitors to register, in some embodiments. Upon leavingthe building and while going through the lobby, visitor is wished a goodday by robot which can be operated by the arrival and departure agent1204.

In some embodiments, upon arrival of a vehicle in a parking lot, theparking agent 1206 is configured to identify a number plate via anautomatic number plate recognition (ANPR) system of the parking lot. Thecar information is provided by the ANPR system to the parking agent1206, based on the car information, the parking agent 1206 assigns aparking bay for the visitor. Furthermore, the parking agent 1206provides a notification (e.g., via the arrival and departure agent 1204)to a host associated with the visitor and/or a receptionist of thearrival of the visitor.

In some embodiments, the parking agent 1206 displays a greeting and thelocation and/or parking number of the allocated parking bay on a displayat the entrance of the parking lot. In some embodiments, the parkingagent 1206 is configured to send a text message to the visitor with hisrespective parking spot. If the visitors parking is full, the parkingagent 1206, rather than assigning and/or alerting the visitor of theirparking spot, directs the visitor to a pre-assigned overflow location.

In some embodiments, in case the vehicle is not recognized, the securitygate guard can register the car with a respective host. The host will benotified by the parking agent 1206, via the arrival and departure agent1204, that a visitor is arriving for the host. In some embodiments, theparking agent 1206, using a vehicle occupancy sensor imbedded in theground of each of multiple parking spots, is configured to determine ifthe visitor has parked in his allocated parking bay and will notify thesecurity guard of correct and/or incorrect parking. If the visitor doesnot follow the instructions and parking in the proper location, theparking agent 1206 is configured to adjust the assigned parking of thevisitor and inform security, in some embodiments. For a user that parksin the incorrect parking spot, the parking agent 1206 is configured toprovide emphasized indication of their parking spot the next time thevisitor arrives at the parking lot.

In some embodiments, during peak time, the parking agent 1206 isconfigured to display, on the greeting screen, not to overstay theparking spot since it is allocated to another visitor after his parkingtime is over. Furthermore, the parking agent 1206 is configured to takeinto consideration early visitor arrival.

In some embodiments, the parking agent 1206 is configured, via theparking spot sensors and/or parking lot cameras, whether a vehicle hasoverstayed its parking time. Overstaying vehicles will be notified tothe host and the security. The parking agent 1206 can also provide anindication of the overstay to the arrival and departure agent 1204 to beregistered by the arrival and departure agent 1204. The visitor willalso receive a notification via text that they have overstayed theirallocated parking time. The next time the same visitor arrives at theparking lot, the parking agent 1206 is configured to display on thegreeting screen a reminder to not overstay their parking lot time.

In some embodiments, pre-booked visitors will have allocated parkingbays. The parking bays maybe reallocated by the parking agent 1206 dueto the late departure of the previous visitors leaving the respectivepre-booked parking bay. In some embodiments, the parking agent 1206 isconfigured to adjust the parking bay allocation to suit the demand needsusing parking lot usage analytics. Upon the visitor leaving the parkinglot, the parking agent 1206 is configured to provide a notification tothe arrival and departure agent 1204 and if required, provide anotification to the host that the visitor has left the building.

Upon leaving the building, a digital display at the gate exit is causedby the parking agent 1206 to display a thank-you message to the visitorfor visiting the building. Furthermore, if weather conditions are poor,the parking agent 1206 is configured to alert the visitor with anindication of the weather conditions, whether the weather conditions areextreme, and prompt the visitor to drive safely.

In some embodiments, an employee can speak with the chatbot 1100 to tellthe chatbot 1100 that the employee is driving to a particular campusbefore the employee departs. The chatbot 1100 provides an indication tothe parking agent 1206 that the employee is on their way and alsoprovides an indication of an estimated time of arrival. Furthermore, thechatbot 1100 can provide another indication of the estimated time ofarrival a few minutes before the employee arrives. The parking agent1206 is configured to monitor the status of all lots texts the chatbot1100 with updates regarding a parking spot to utilize. The chatbot 1100,in some embodiments, provides a notification to the employee to drive tothe appropriate lot. In some embodiments, the chatbot 1100 interfaceswith a speaker system of a car of the employee and provides theindication to the employee via the speaker system.

In some embodiments, an employee can registers a visitor with theparking agent 1206 for a next day with the visitor management systemfrom the arrival and departure scenario, and includes a phone number ofthe visitor. In some embodiments, the parking agent 1206 is configuredto obtain a parking reservation for the visitor and texts a five-digitcode along with the address and a link with directions to the parkingramp the visitor is assigned to. If the parking is not nearby a meetinglocation of the visitor, the parking agent 1206 arranges a shuttle andincludes the shuttle information in the text.

In some embodiments, if any unidentified person is entering a buildingwith an identified employee, facility operations agent 1208 isconfigured to provide an alert a receptionist and indicate the presenceof the unidentified person with a visible light indication. Furthermore,the facility operations agent 1208 is configured to log in the entry andexit time of each employee and integrate this information with varioushuman resource systems. The time login can take place from any of thebuilding entrances and/or exits.

In some embodiments, the facility operations agent 1208 is configured tolog out visitors exiting along with employees automatically via facialrecognition even if the visitor exits from a non-visitors gate of thebuilding. In some embodiments, if an employee wishes to find thelocation of their colleague within a building, and depending onpermission and/or roles assigned to the employee, the facilityoperations agent 1208 is configured to identify the location of thecolleague via facial recognition by closed-circuit television (CCTV)cameras throughout the building and/or using wireless beacon trackingsystems.

Throughout the building some doors may have wireless access controlusing smart phones, that will be linked with the employees identify. TheCCTV cameras may provide camera feeds to the facility operations agent1208, the facility operations agent 1208, via the camera feeds, isconfigured to detect the suspicious behavior. The system will notify thesecurity of any behavior and identify the person committing it. Thiswill include in and/or out of the building. The facility operationsagent 1208 is configured to detect out of hours suspicious behaviorsbased on the camera feeds.

In some embodiments, all cameras throughout the building will havefacial recognition capabilities and the facility operations agent 1208is configured to track occupants through the building and provide theirlast location when asked by the security or authorized personnel. Thefacility operations agent 1208 is configured to be able to instantly saywho is and who is not within the building and will be able to track thehistory of movement.

If a visitor arrives with a staff member, the receptionist will updatethe visitor into the arrival and departure agent 1204 with the detailsand host information of the visitor. If the visitor leaves with anemployee from a staff exit, regardless from where he entered, thefacility operations agent 1208 is configured to recognize the visitorand update the reception and arrival and departure agent 1204 with anindication that the visitor has left the building.

In some embodiments, when and if required for future access control, thefacility operations agent 1208 is configured to add and/or removedoor-locks without having to do any rewiring. If any VIP is entering thebuilding whether form a visitor entrance or a staff entrance, thefacility operations agent 1208 is configured to detect, via facialrecognition, the presence of the VIP and communicate with the arrivaland departure agent 1204 to execute a VIP protocol and inform therelevant staff via the chatbot 1100. In some embodiments, the facilityoperations agent 1208 is configured to receive information from a Wi-Fiscanner, a smart phone reader, a face recognition system continuously tovalidate employees and visitors while they are in the building and/orand updates the location of visitor and employee while they are withinthe building.

In some embodiments, the workplace design agent 1210 is configured toprovide a digital twin display of all spaces of a building in a threedimensional map. In some embodiments, the workplace design agent 1210 isconfigured to filter which space information to display (e.g.,temperature, lighting, occupancy, etc.). The facility operations agent1208 is configured to filter the information by floor and by zone, insome embodiments.

In some embodiments, the workplace design agent 1210 is configured todetect locations of the building with data below the acceptable range(under populated areas, unused lighting or chilling). The workplacedesign agent 1210 is configured, in some embodiments, to automaticallyswitch off or dim lights in under-populated areas. In some embodiments,the workplace design agent 1210 is configured to automatically reducechilling in under populated areas, in some embodiments.

In some embodiments, the workplace design agent 1210 is configured todetect locations of the building with data increasing above theacceptable range (increased temperature, over populated areas, etc.).The workplace design agent 1210 is configured to automatically turn oncooling in the detected locations of the building. The workplace designagent 1210 is configured to analyze the occupancy and temperature ofdifferent areas of the building and compare different areas of thebuilding. The workplace design agent 1210 is configured to recommendemployees in real-time to use other locations that are under populatedto balance the occupancy, in some embodiments. In some embodiments, theworkplace design agent 1210 is configured to provide enough power,temperature, and lighting for employees to use the new locations.

Workplace design agent 1210 is configured to analyze the occupancypatterns per working hours and working days, in some embodiments. Theworkplace design agent 1210, is configured, in some embodiments, toanalyze and understand each employee space utilization pattern/behaviorand will recommend enhanced utilization behaviors to each employee basedon temperature and lighting preferences.

In some embodiments, the workplace design agent 1210 is configured toanalyze the occupancy patterns per working hours and working days. Theworkplace design agent 1210 is configured to identify frequentover-populated and frequent under-populated areas despite itsrecommendations to Employees. In some embodiments, facility managerswill be able to take decisions to relocate certain functions that impactthe space occupancy.

In some embodiments, employees will be able to control the spaceambience (lighting and temperature) through the chatbot 1100. Theworkplace design agent 1210 is configured to learn the employeepreferences and in the future, automatically adjust the space ambienceaccordingly, in some embodiments. If different employees in the samespace have different preferences, the workplace design agent 1210 isconfigured to automatically average the ambience accordingly.

In some embodiments, if required, the workplace design agent 1210 isconfigured to identify individuals who have common ambient preferences.In some embodiments, the workplace design agent 1210 is configured tosocially connect the identified individuals together and recommend themto hangout in the same areas to optimize power utilization.

In some embodiments, the workplace design agent 1210 is configured tosend quick questionnaire to employees every time they use a new spaceasking them “How productive was your day?” In some embodiments, theworkplace design agent 1210 is configured to store this data in thepreference profile of the employee and understand whether the preferenceof the employee actually drives him to be productive. In someembodiments, the workplace design agent 1210 is configured to learn whenand where employees are actually productive and will recommend them inthe future to select the best ambience and the best location.

In some embodiments, the workplace design agent 1210 is configured toagent use a schedule of an employee to determine employees coming intobuilding soon. In some embodiments, the workplace design agent 1210 isconfigured to pre-adjust temperature, lighting, and/or air qualitybefore arrival of an employee. In some embodiments, a smart spaceapplication and/or chatbot 1100 notifies occupants that there is anemergency (like fire alarm). In some embodiments, the workplace designagent 1210 is configured to send notifications to emergency respondersif people are in danger in the space.

For a facility manager, energy consumption information is reported invarious formats and timelines by the workplace design agent 1210. Theformats illustrate energy savings month-by-month and year-by-yearthrough a space management dashboard. The workplace design agent 1210 isconfigured to provide artificial intelligence powered back end analyticsand can recommend energy optimization actions.

Furthermore, for a facility manager, the workplace design agent 1210 isconfigured to report utilization data in various formats and timelines.The report may illustrate space usage by day, week, month, and year. Thereport may illustrate space usage to show actual space usage vs. spacereservations from the meeting reservation system though the spacemanagement dashboard. In some embodiments, for an employee and/orvisitor, the employee and/or visitor can report and create servicerequests to facility manager through a mobile app and/or the chatbot1100. In some embodiments, the employee and/or visitor can request anyfacility service including cleaning, catering, supply refill, repair ofoffice equipment via the agents 1202-1214.

In some embodiments, the facility operations agent 1208 is configured tointegrate with all building, elevator systems, information technologysystems, etc. in a single Data-Lake. Operator can, via the facilityoperations agent 1210, customize and/or build any dashboard componentsto display any current status information and/or reporting forinformation of the data lake. Furthermore, in some embodiments, thefacility operations agent 1208 is configured to continuously optimize onthe energy consumption, efficiency, and/or improve the carbon footprintof a building. The facility operations agent 1208 is configured, in someembodiments, to measure improvements on saving continuously measuredagainst key performance indicator compliance.

In some embodiments, the facility operations agent 1208 is configured toperform artificial intelligence based self-healing in which a buildingsystem will take directly decisions to remedy a fault and/or compensateby adjusting adjacent equipment and/or informing a facility manager.When a fault occurs, the facility operations agent 1208 is configured tovalidate the alarm, inform the facility personnel via a hand-held devicepush notification or text message depending on the urgency of the fault.

In some embodiments, facility operation agent 1208 includes access toall the building mechanical, electrical, and/or plumbing information ina three dimensional representation of the building. The operator will beable to zoom in digitally onto any of the device and equipment and viewthe current status, history, and/or Datasheets, in some embodiments. Thethree dimensional digital twin will also contain information of allassets, employee and occupancy data, space characteristics, and/orusage. The three dimensional digital twin may illustrate thedistribution of employees and employees and a heatmap of energyconsumption and efficiency.

In some embodiments, the facility operations agent 1208 is configured tocontinuously improve and save time by automating routine tasks,streamlining processes, and/or eliminating redundant steps to meet theworkflow needs of staff while complying with various regulations andusing collaborative work environment. In some embodiments, the facilityoperations agent 1208 is configured to monitor and/or improve servicequality by making it faster and easier for facility managementprofessionals to access and share information for knowledge-drivendecisions that improve outcomes and/or by helping to make sure that theright resources are in the right place at the right time.

In some embodiments, the facility operations agent 1208 is configured toidentify abnormal behavior of equipment and/or devices. Once anequipment has been identified the facility operations agent 1208 isconfigured to inform facility management personnel and take self-healingactions. In some embodiments, the facility operator should be able toreview data without opening other operational applications via a singleintegrated collaboration work environment. Tasks, budget and resourcesare synchronized and/or syndicated in real time with other enterpriseresource planning (ERP) and project management systems as required, insome embodiments. In case of low inventory of any of the equipment ormaterials, or unavailability of spare parts, facility operations agent1208 will integrate with an inventory and be able to generate purchaserequisitions and/or orders, in some embodiments.

In some embodiments, an employee can ask the chatbot 1100 if any of thenearby dining options are serving an particular lunch entry on a currentand/or future day. The chatbot 1100 is configured to determine whetherany nearby restaurants are serving the requested type of lunch entry aswell as nutrition information for the entry. If an employee is hosting aguest, the employee can register the guest with the arrival anddeparture agent 1204. The arrival and departure agent 1204 is configuredto text the visitor with a parking code and directions to the buildingand any other information we need if this is a first-time visitor. Thevisitor picks up a badge when they arrive in the lobby and the badge isregistered in the food and beverage agent 1212 at the same time. Whenthe visitor orders food via the food and beverage agent 1212, thevisitor “pays” using their badge and all billing is automaticallyapplied to a cost center application associated with the user.

In some embodiments, an order bot orders and coordinates a time fordinner. The order bot can be a kiosk, a mobile robot, the food andbeverage agent 1212, etc. The order bot puts food orders into the foodand beverage agent 1212 so that the food and beverage agent 1212 canfacilitate prompting automatic food preparation systems and/or foodpreparation individuals to prepare the food so that the food is readyfor at an arrival time of the individuals placing the order. The orderbots can be configured to utilize signage at a tables and occupancysensors to detect a free table shortly before the individuals placingthe orders arrive reserves the table for the individuals. The bot textsthe individual the table location and at time to pick up the food.

In some embodiments, an employee can tell the chatbot 1100 a time thatthey would like to leave an office, e.g., between 8:00 PM and 8:30 PM.The chatbot 1100 and/or the transportation agent 1214 is configured toidentify employees who live nearby the requesting employee and areleaving leave about the same time. The chatbot 1100 and/or thetransportation agent 1214 is configured to arrange a ride sharingservice with the two employees. If the ridesharing is through a taxiservice, the cost of transportation can be split between the employeesand/or a sponsor (e.g., a business) can cover the cost oftransportation.

A facility manager may realize that there is a shortage of docklessbikes or electric scooters near a building. The facility manager can askthe chatbot 1100 to message people who may have meetings in and/or nearmy building in the next few hours to ride a bike and/or scooter over torebalance the dock. The bike-riders are rewarded with an item from acafeteria or through progress on their fitness goals

If an employee typically takes a bus, tram, bike or other form oftransportation to work, the employee may not be associated with aparking spot. Furthermore, if the employee is physically injured andunable to walk long distances, the employee can ask the chatbot 1100 toarrange temporary parking on campus in a spot as close to the doors aspossible to minimize walking. The chatbot 1100 can communicate therequest to the transportation agent 1214 and the transportation agent1214 can be configured to facilitate the request.

A visitor may be staying at a hotel near a building. The arrival anddeparture agent 1204 can be configured to texts a quick response (QR)code that will pay for transportation fares from the hotel to thebuilding and/or from the building back to the hotel, provide directionsthe transportation (e.g., what bus to use, what tram station to use,what train line to use, etc.).

Referring now to FIG. 13, the space graph database 1120 is shown ingreater detail, according to an exemplary embodiment. The space graphdatabase 1120 includes entities 1300-1328 (stored as nodes within thespace graph database 1120) describing spaces, devices, people (e.g.,business employees), and agents implementing artificial intelligence.Furthermore, relationships are shown between the entities 1300-1328directionally describing relationships between two of the entities1300-1328 (stored as edges within the space graph database 1120). Withthe space graph database 1120, various control application systemsand/or agents can receive a description of what types of actions to takefrom a certain device, what the current status of a room is (e.g.,occupied or unoccupied), etc. The space graph database 1120 is flexiblein managing new entities and relationships that are very expensive witha conventional database such as a relational database management system(RDBMS).

As an example, the space graph database 1120, as shown in FIG. 13,illustrates an office space called “B7F5 North RM2” of a building. Insome embodiments, the space graph learning service 1104 extracts thenodes of the space graph database 1120 from various data sourcesincluding a building automation system, a security system, a fire alarm,human resources system, and/or building information model (BIM) files(e.g., the building data sources 1102) through an entity name matchingprocess. Furthermore, semantic relationships can be extracted from thebuilding information by the space graph learning service 1104. Eventhough the nodes and edges of the space graph database 1120 aregenerated based on the determination of existing entities andrelationships between entities, many relationships may be missing butcan be discovered from collected building data that is ingested into thespace graph database 1120, e.g., sensing and actuation data. As anexample, an employee entity, John Smith, can be found from a cardholderdatabase but a relationship between John Smith and his office cannotinitially be determined. Similarly, the space graph learning service1104 is configured to identify a door lock and card reader but withoutoperational data, the space graph learning service 1104 cannot determinewhether a card reader “B7F5Cr2” is controlling a door lock “B7F5 Door2”.

For example, entity 1300, a lighting element referred to as “Light 0003”has a directional relationship to the space 1314 referred to as “B7F5North RM2.” The relationship may be an edge “hasLocation” indicatingthat the entity 1300 has a location, the entity 1314. Furthermore, asecond edge “contains” from the entity 1314 to the entity 1300 indicatesthat the space 1314 includes the entity 1300 within it.

As another example, space 1316 and space 1314 have relationships betweenthem. A directional relationship from the space 1316 to the space 1314indicates that the space 1316 is above the space 1314, i.e., that space1316 is on a floor within a building above the space 1314. Furthermore,a second relationship from the space 1314 to the space 1316 indicatesthe location of the space 1314 relative to the space 1316, therelationship is a below relationship indicating that the space 1314 isbelow the space 1316.

In some embodiments, only a single relationship exists between entities.For example, door lock 1322 has a location within the space 1314 asrepresented by the “hasLocation” edge. As shown, there is norelationship between the space 1314 to the door lock 1322. Since doorlock 1322 has a location within the space 1314, the space graph learningservice 1104 could be configured to automatically add a correspondingrelationship from the space 1314 to the door lock 1322, e.g., a“contains” edge indicating that the door lock 1322 is contained withinthe space 1314. Furthermore, additional relationships could be addedbetween the door lock 1322 and the space 1314 in addition to the“hasLocation” and “contains” edges. For example, if the space graphlearning service 1104 is configured to add a “controls” or“allowsAccessTo” edge from the door lock 1322 to the space 1314indicating that the door lock 1322 is a security measure controllingaccess to the space 1314.

The entities 1306-1312 are shown in dashed lines indicating that theentities 1306-1312 are related to environmental conditioning. In thisregard, each of the node of the space graph database 1120 may representwhat subsystem, e.g., HVAC, security, fire, etc. that they belong to. Insome embodiments, each of the entities 1306-1312 include relationshipsto a subsystem node. For example, a HVAC subsystem node may exist whichincludes a set of relationships “has” to entities 1306-1312 indicatingthat the HVAC subsystem has the entities 1306-1312. The entities1306-1312 may each include a “isAPartOf” relationship from the entities1306-1312 to the HVAC subsystem node to indicate that the entities1306-1312 form an HVAC subsystem.

Comfort agent 1320 and meeting agent 1318 are configured to providecontrol of the space 1314, in some embodiments. The comfort agent 1320is configured to control environmental conditions (e.g., temperature,lighting, humidity, etc.) for the space 1314, in some embodiments. Themeeting agent 1318 can be configured to perform scheduling of meetingsand/or communication with meeting organizers and/or meeting attendees.The meeting agent 1318 can be the same as and/or similar to the meetingroom agent 1202. The comfort agent 1320 and the meeting agent 1318 eachinclude an edge to the space 1314 indicating “controls,” a semanticrelationship indicating that the agents 1320 and 1318 are assigned toperform operations for the space 1314. The relationships “has Agent” canbe the edges between the space 1314 to the agents 1318 and 1320representing that the space 1314 has agents that it is assigned.

The comfort agent 1320 is configured to generate a schedule 1326. Theschedule 1326 can be a temperature schedule for the space 1314. In someembodiments, the space graph learning service 1104 is configured tolearn a new relationship from existing relationships for the space graphdatabase 1120. For example, the agent 1320 can cause the space graphdatabase 1120 to include a new node for the schedule 1326 in response tobeing run for a first time. The space graph learning service 1104 canidentify that the agent 1320 controls the space 1314 and also that thethermostat 1306 has a relationship to other equipment, VAV box 1310,that feeds the space 1314. Based on these relationships, the space graphlearning service 1104 can add edges “hasSchedule” and “isUsedBy” betweenthe thermostat 1306 and the schedule 1326 to indicate that thethermostat 1306 should run the schedule 1326 and that the schedule 1326is used by the thermostat 1306.

Furthermore, the space graph database 1120 includes datapoints within itand their relationship to other pieces of equipment. For example,temperature setpoint 1308 may be an actuation point of thermostat 1306as represented by the edge “hasActuationPoint” from the thermostat 1306to the temperature setpoint 1308. The temperature setpoint 1308 controlsVAV box 1310, i.e., changes in the value of the temperature setpoint1308 adjusts the operation of the VAV box 1310. Furthermore, an edgebetween the VAV box 1310 and the temperature setpoint 1308 indicatesthat the VAV box 1310 is operated based on the temperature setpoint 1308“isOperatedBy.”

In some embodiments, the entities 1300-1328 have components. Forexample, VAV box 1310 may include the damper 1312. In this regard, anedge between the VAV box 1310 and the damper 1312 may indicate that thedamper is a part of the VAV box 1310 “isAPartOf.” Furthermore, an edgebetween the VAV box 1310 and the damper 1312 may indicate that the VAVbox 1310 has the damper 1312 as a part, i.e., “hasPart.”

Referring now to FIG. 14, the space graph database 1120 is shown wherethe space graph learning service 1104 learns new relationships for thespace graph database 1120 and adds new corresponding edges to the spacegraph database 1120, according to an exemplary embodiment. The graphlearning service 1104 is configured to apply multiple different forms oflearning to the space graph database 1120. The space graph learningservice 1104 is configured to automatically generate the space graphdatabase 1120, deduce and/or fill-in missing relationships after thespace graph database 1120 is generated, deduce new entity types fornewly added entities, and/or predict a future state of an entity.

One example of the learning that the space graph learning service 1104is configured to perform is to observe co-occurrences of discrete eventswithin predetermined intervals overtime. The table below indicatesevents that, when occurring within a predefined amount of time, indicatethat the employee 1304 “John Smith” is related to the space 1314 “B7F5North RM2.” The events are determined by the space graph learningservice 1104 based on predicate logic, wherein the predicate of thelogic used to generate the events are the edges of the space graphdatabase 1120, the actions or operational data can be data ingested intothe space graph database 1120, and the entities referred to by the logiccan be the nodes of the space graph database 1120.

Event No. Description Predicate Logic-based Description 1 B7F5 CR 2:CardSwap(CardHolder(“John Smith”)) && John Smith's CardReader(“B7F5 CR2”) && card swiped HasLocation(“B7F5 CR 2”, ““B7F5 North RM 2”) 2 B7F5Door 2: DoorOpenNormal(“B7F5 Door 2”) && Normal Door HasLocation(“B7F5Door 2”, Unlock ““B7F5 North RM 2”) 3 Light: On(“Light 003”) && Light003 ON HasLocation(“Light 003”, ““B7F5 North RM 2”) 4 Smart TV:TurnOn(“TV 001”) && TV 001 ON HasLocation(“TV 001”, ““B7F5 North RM 2”)

The space graph learning service 1104 is configured to determine, basedthe logic rules and the data to the space graph database 1120 (e.g., thenodes, edges, and operational data ingested into the space graphdatabase 1120) the occurrence of one or more events. If the space graphlearning service 1104 determines the one or more events occurring withina predetermined time interval, the space graph learning service 1104 canimplement a process to reasoning about new potential relationships amongentities. For example, if the space graph learning service 1104 capturesa predefined amount the events of the table below every hour for a setnumber of hours, a relationship can be determined between the entityjohn smith 1304 and the space 1314. Furthermore, based on behaviorsand/or patterns in occupancy of the space 1314 determined from the rulesof the table below, the agent 1320 can mine occupancy patterns for thespace 1314 and generate and/or update the schedule 1326 to preheatand/or precool the space 1314.

For example, the first rule,

CardSwap(CardHolder( “John Smith”)) && CardReader(“B7FS CR 2”) &&HasLocation(“B7F5 CR 2”, ““B7F5 North RM 2”)may create an event that a particular entity, employee 1304 has enteredthe space 1314. The rule may indicate that if a card swap occurs foremployee 1304 and the card reader at which the card swap occurred is thecard reader 1324 and that the card reader 1324 has a location of thespace 1314, an event that the employee 1304 has entered the space 1314.The rule can be tested by the space graph learning service 1104 via therelationships, nodes, and ingested data of the space graph database1120. A similar event can be created for any user entity of the spacegraph database 1120. If the space graph learning service 1104 determinesthat the event has occurred a predefined mount of times within apredefined period of times, e.g., the event has occurred twenty timeswithin a week, this may indicate that there is a relationship betweenthe employee 1304 and the space 1314.

As another example, the second rule,

DoorOpenNormal(“B7F5 Door 2”) && HasLocation(“B7F5 Door 2 ”, ““B7F5North RM 2”)may create an event that a particular door of a conference room has beenopened, specifically, the door lock 1322 has opened a door of space1314. If a door is opened, based on the door lock 1322 unlocking, andthe door lock 1322 has a location of the space 1314, this may indicatethat a door of the space 1314 was opened. If the first and second rulesdescribed above indicate that the door of the space 1314 has opened andthat a particular user has used a card reader to enter the space 1314,the space graph learning service 1104 can determine that the card reader1324 controls the door lock 1322. For example, if a predefined number ofthe first and second rules occurring within predefined amounts of timefrom each other are recorded by the space graph learning service 1104,the space graph learning system can cause the space graph database 1120to add an edge between the door lock 1322 and the card reader 1324.

As another example, the third rule,

-   -   On(“Light 003”) && HasLocation(“Light 003”, ““B7F5 North RM 2”)        3 and fourth rule,    -   TurnOn(“TV 001”) && HasLocation(“TV 001”, ““B7F5 North RM 2”)        may create events that an activity within the space 1314 has        occurred, indicating occupancy. In some embodiments, the comfort        agent 1320 analyzes the data of the space graph database 1120 to        determine whether the third or fourth rules have occurred and is        configured to update and/or generate the schedule 1326 based on        patterns of the third or fourth events over time. For example,        if the fourth event occurs at 9 A.M. every working day, the        agent 1320 can adjust the comfort schedule 1326 to be a preheat        and/or precool the space 1314 to an appropriate temperature by 9        A.M. on working days.

The third event may indicate that the lighting device 1300 turns on andhas a relationship between the lighting device 1300 and the space 1314.The light turning on can be determined based on operational dataingested into the space graph database 1120 while the relationship canbe determined from the existence of the edge between the lighting device1200 and the space 1314. Similarly, if the smart TV 1302 turns on and arelationship exists between the smart TV 1302 and the space 1314, thesmart TV 1302 turning on in the space 1314 can be determined. Similarly,the operational data indicating that the smart TV 1302 has turned on canbe determined from operational data ingested in to the space graphdatabase 1120 while the relationship between the smart TV 1302 and thespace 1314 can be determined from the “hasLocation” edge existingbetween the smart TV 1302 and the space 1314.

Referring to FIG. 15, the space graph database 1120 is shown where thespace graph learning service 1104 replaces a temporary relationship witha permanent relationship, according to an exemplary embodiment. Asdescribed with reference to FIG. 14, rules can be utilized to determinea relationship between entities, specifically, using a few days ofoperational data ingested into the space graph database 1120observation, the space graph learning service 1104 is configured todiscover the third event (e.g., the lighting device 1300 turning on inthe space 1314), the first event (e.g., the employee 1304 accessing thecard reader 1324 with an access card), and the second event (e.g., adoor of the space 1314 opening via the door lock 1322) occur at apredefined frequently. The relationship can be added as a temporaryrelationship between the employee 1304 and the space 1314.

The space graph learning service 1104 can periodically test thetemporary relationship to determine whether the temporary relationshipshould be converted into a permanent relationship. For example, atperiodic intervals, the space graph learning service 1104 is configuredto analyze newly ingested operational data and/or newly added entitiesand/or relationship of the space graph database 1120 to generate aconfidence level that the permanent relationship should exist and, inresponse to determine a confidence level above a predefined amount,replace the temporary relationship with a permanent relationship. Insome embodiments, the space graph learning service 1104 replaces thetemporary relationship with two permanent relationships, i.e., two edgesbetween the employee 1304 and the space 1314.

For example, the employee 1304 may include an edge to the space 1314which indicates that the employee is associated with the space 1314,e.g., a “hasLocation” edge. Furthermore, an edge from the space 1314 tothe employee 1304 can be added to indicate that the space 1314 isassigned to the employee 1304 (e.g., the space 1314 is an office of theemployee 1304), this edge may be the “assignedTo” edge.

Referring now to FIG. 16, the space graph database 1120 is shown wherean impact relationship is identified and added to the space graphdatabase 1120 by the space graph learning service 1104, according to anexemplary embodiment. The “impact” or “relatedTo” edge can be identifiedby the space graph database 1120 by analyzing operational data, nodes,and edges of the space graph database 1120. The impact relationship canbe an indication that operations performed by a first entity indirectlyaffect a second entity. For example, if the VAV box 1310 operates tocondition the space 1314 this may indirectly affect the space 1328.

The impact relationship can assist the agents of the space graphdatabase 1120 in implementing energy efficient control algorithms. Forexample, the space graph learning service 1104 is configured todetermine, based on observing air volume changes for multiple zones,that the operation of the VAV box 1310 affects the space 1328 (the samesupply air is used for both the space 1314 and the space 1328 and thusoperation of the VAV box 1310 affects both spaces). By analyzing theimpact edges of the space graph database 1120, the agent 1320 canoptimize the comfort schedule 1326 to take into account the space 1328,for example, the comfort agent 1320 can coordinate with another comfortagent of the space 1328 to generate complimentary schedules of the space1314 and the space 1328 which utilize the impact relationship to reduceoverall energy consumption.

Referring now to FIG. 17, a process 1700 for generating the space graphdatabase 1120 by the space graph learning service 1104 is shown,according to an exemplary embodiment. In some embodiments, the spacegraph learning service 1104 is configured to perform the process 1700 togenerate the space graph database 1120. Instructions stored on one ormore memory devices and executed on one or more processors can beconfigured to implement the space graph learning service 1104 and thegeneration of the space graph database 1120. Furthermore, any processingdevice, system, and/or software component as described herein can beconfigured to perform the process 1700.

In step 1702, the space graph learning service 1104 receives buildingdata from one or more building data sources of a building, e.g., thebuilding data sources 1102. The building data sources 1102 can be BMSsystems, point descriptions files, data files including entities andrelationships between the entities, security system data, humanresources data, access control system data, etc.

In step 1704, the space graph learning service 1104 is configured togenerate the space graph database 1120. The space graph database 1120can include one or more entities, included as nodes within the spacegraph database 1120, and one or more relationships, included asdirectional edges between the nodes within the space graph database1120. The entities can represent physical and/or virtual components of abuilding for which the data is received in the step 1702. In someembodiments, the space graph learning service 1104 parses the buildingdata of the step 1702 to identify each entity represented in thebuilding data and identifies relationships between the entities.

In step 1706, new building data can be received from the building datasources of the building representing operations of the physical and/orvirtual components. In some embodiments, the new building data isoperational data for a physical component, e.g., temperaturemeasurements by a temperature sensor, access requests to an accesscontrol system, elevator request for an elevator, damper position of aVAV box, etc. Furthermore, the building data can represent operation ofvirtual components. For example, a temperature setpoint being adjustedby a user, a performance metric (e.g., energy usage metric) changing,etc. The new building data can be representative of various values forthe various nodes of the space graph database 1120.

In step 1708, the space graph learning service 1104 is configured toingest the new building data into the space graph database 1120. Forexample, the space graph database 1120 can be updated to store valuesrepresenting operations, data collections, and/or adjustments to thephysical and/or virtual components represented as nodes within the spacegraph database 1120. New data can be continually received by the spacegraph learning service 1104 from the building data sources 1102 andingested into the space graph database 1120. In this regard, the steps1706 and 1708 can be performed continuously and/or periodically.

Referring now to FIG. 18, a process 1800 for updating a space graphdatabase 1120 by the space graph learning service 1104 with new entitiesand/or new relationships between entities is shown, according to anexemplary embodiment. In some embodiments, the space graph learningservice 1104 is configured to perform the process 1800 to generate thespace graph database 1120. Instructions stored on one or more memorydevices and executed on one or more processors can be configured toimplement the space graph learning service 1104 and the generation ofthe space graph database 1120. Furthermore, any processing device,system, and/or software component as described herein can be configuredto perform the process 1800.

In step 1802, the space graph learning service 1104 received newbuilding data from one or more building data sources of a building foran existing space graph database 1120. For example, the space graphlearning service 1104 can received new building data from the buildingdata sources 1102 indicating the operation of the building, i.e.,operational data for the entities of the space graph database 1120. Thespace graph learning service 1104 can ingest the new building data intothe space graph.

In step 1804, the space graph learning service 1104 can determine basedon the new building data of the step 1802 one or more new relationshipsfor the space graph, the space graph including one or more relationshipsbetween one or more entities, and data for the one or more entities. Insome embodiments, the space graph learning service 1104 analyzes thenewly ingested building data of the space graph database 1120 utilizingvarious relationship event rules (e.g., the rules as described withreference to FIG. 14) and/or machine learning models to generate the newrelationships.

In step 1806, the space graph learning service 1104 determinesrelationship type for the one or more new relationships. For example,the space graph learning service 1104 can identify the node types ofnodes of the space graph database 1120 for which the new relationshipsare for and generate the relationship types based on the node types. Forexample, for a thermostat and a room, the space graph learning service1104 can add a “controls” and “isLocatedIn” relationship types betweenthe two nodes. In some embodiments, the relationship types are based onan analysis of the new data. For example, if the new building dataindicates that operating a particular actuator device changestemperature measured by a particular sensor device, this may indicatethat the impact relationship should be generated.

In step 1808, the space graph learning service 1104 can generate the oneor more new relationships based on the relationship types. The generatedrelationships may be edges for multiple nodes. In step 1810 cause thespace graph to include and/or store edges representing the one or morenew relationships.

In step 1812, the space graph learning service 1104 is configured toutilize the new building data, which may be ingested into the spacegraph database 1120. In some embodiments, new data can be received froma building subsystem, the new data indicating a new point and/or newdevice. In some embodiments, the new data may represent a newlyinstalled device. In this regard, the space graph learning service 1104is configured to parse the data with machine learning models,dictionaries, etc. to identify the new entity. In step 1814, the spacegraph learning service 1104 can update the space graph database 1120. Insome embodiments, updating the space graph database 1120 includescausing the space graph database 1120 to include and/or store new nodeswithin the space graph database 1120. Furthermore, similar to steps1808-1810, based on the new entities and/or the new building data (ordata received after the new building data), additional new relationshipscan be generated. The relationships can be relationships between two newentities and/or between a previous entity and/or a new entity. In thisregard, a new entity could be added to the space graph although the dataused to generated the new entity may not indicate any relationships forthe entity. After more data is received (e.g., data generated by the newentity), based on the space graph with the new entity and/or the newdata, new relationships for the new entity can be determined.

Referring now to FIG. 19, a process 1900 for updating control algorithmsof a space graph database 1120 based on relationship and entity updatesto the space graph database 1120 that can be performed by the spacegraph learning service 1104, according to an exemplary embodiment. Insome embodiments, the space graph learning service 1104 is configured toperform the process 1900 to generate the space graph database 1120.Instructions stored on one or more memory devices and executed on one ormore processors can be configured to implement the space graph learningservice 1104 and the generation of the space graph database 1120.Furthermore, any processing device, system, and/or software component asdescribed herein can be configured to perform the process 1900. Thesteps 1802-1814 of the process 1800 are included in FIG. 19 and aredescribed with reference to FIG. 18.

In step 1902, one or more agents of the space graph database 1120 canassess the one or more new relationships and/or new entities to generatecontrol updates. The one or more agents can periodically search thespace graph database 1120 for new relationships and/or new entities. Insome embodiments, the one or more agents can be triggered to performupdates in response to new relationships and/or new entities being addedto the space graph database 1120.

In some embodiments, the control updates can be updates to an occupancyschedule. If it is determined that a television is located within aparticular zone, operation of the television may indicate occupancy ofthe zone. The one or more agents can identify the operations of thetelevision to identify an occupancy pattern. The occupancy pattern canbe an update to an existing occupancy schedule.

In step 1904, the one or more agents can update one or more controlalgorithms of the space graph database 1120 with the one or more controlupdates. For example, the one or more agents can update temperaturesetpoints of a comfort schedule. In other embodiments, the one or moreagents can activate or deactivate a control algorithm. For example, thecontrol algorithm can be linked to an office that is not in use.However, if a user is linked to the office, this may indicate that theuser will be and/or is using the office and a comfort schedule should beimplemented for the office. In step 1906, the one or more agents canoperate pieces of building equipment based on the one or more controlalgorithms that are updated to control one or more environmentalconditions of the building.

Referring now to FIG. 20, a process 2000 for updating a space graphdatabase 1120 with temporary relationships by the space graph learningservice 1104 with new entities and/or new relationships between entitiesis shown, according to an exemplary embodiment. In some embodiments, thespace graph learning service 1104 is configured to perform the process2000 to generate the space graph database 1120. Instructions stored onone or more memory devices and executed on one or more processors can beconfigured to implement the space graph learning service 1104 and thegeneration of the space graph database 1120. Furthermore, any processingdevice, system, and/or software component as described herein can beconfigured to perform the process 1900. The steps 1802-1804 of theprocess 1800 are included in FIG. 20 and are described with reference toFIG. 18.

In step 2002, based on the one or more new relationship, the space graphlearning service 1104 adds temporary relationships to the space graphdatabase 1120. The temporary relationship may be a placeholder to add aformal relationship, a more permanent relationship (although it could beupdated or deleted in the future) can be determined and/or what type ofrelationship should be added for the permanent relationship. In someembodiments, the space graph learning service 1104 causes the spacegraph database 1120 to store an edge between entities of the space graphdatabase 1120 which may have the relationship and cause the edge to belabelled as a temporary edge.

In step 2004, new building data can be received from the one or morebuilding data sources. In step 2006, the space graph learning service1004 ingests the new building data into the space graph database 1120and/or otherwise analyzes the ingested and/or new building data todetermine whether the temporary relationship should be replaced with apermanent relationship. In some embodiments, the edge is associated withand/or stores a confidence level. The confidence level may be based onmachine learning and/or relationship rules. For example, if an eventindicating a relationship occurs every hour, this may indicate a firstlevel for the confidence level. However, if the event indicating therelationship occurs ten times every hour, this may indicate a secondlevel for the confidence level. If the confidence level is greater thana predefined amount, the temporary relationship can be determined to bea permanent (e.g., a formal) relationship.

Furthermore, the space graph database 1120 can determine the nature ofthe permanent relationship. For example, based on the new data, the typeof the relationship can be discovered. In some embodiments, the type ofthe relationship is based on the types of the nodes which the permanentrelationship connects. In step 2010, the space graph learning service1104 is configured to remove the temporary relationship can replace thetemporary relationship with one or multiple permanent relationships withthe permanent relationship types determined in the step 2008.

Referring now to FIG. 21, a process 2100 for updating a space graphdatabase 1120 by the space graph learning service 1104 with new entitiesand/or new relationships between entities is shown, according to anexemplary embodiment. In some embodiments, the space graph learningservice 1104 is configured to perform the process 2100 to generate thespace graph database 1120. Instructions stored on one or more memorydevices and executed on one or more processors can be configured toimplement the space graph learning service 1104 and the generation ofthe space graph database 1120.

In step 2102, the space graph learning service 1104 receives newbuilding data from one or more building data sources of a building. Thebuilding data can indicate operations of entities of the space graphdatabase 1120. The building data can indicate the result of theoperation of various control algorithms performed by agents of the spacegraph database 1120. For example, if a comfort agent causes thermostatto control temperature in a zone, the measured temperature for the zonecan be included by the building data. Furthermore, the building data caninclude measured temperature for other zones of the building.

In step 2104, the space graph learning service 1104 identifies one ormore impact relationships between entities of the space graph database1120 caused by operation of the control algorithm. For example, theschedule of controlling temperature for the zone may also changetemperature in a neighboring zone. For example, if a single air ductfeeds a first zone and a second zone, operating a VAV of the first zoneconnected to the air duct may impact control of the second zone. In step2106, the space graph learning service 1104 can generate an impactrelationship (e.g., an edge identified as an impact edge) to be added tothe space graph.

In step 2108, the space graph learning service 1104 can update the spacegraph database 1120 with the impact relationship, the impactrelationship indicating the affects cause by the control algorithmbetween at least some of the entities. In some embodiments, thecontrolled component of the space graph operated based on the controlalgorithm is linked to the impacted entity. For example, the VAV box ofthe first zone is linked to the second zone via the impact relationship.This edge, which can be added between entities for the VAV box and thesecond zone, can be utilized to adapt one or more control algorithms.

In step 2110, one or more pieces of building equipment can be operatedbased on the space graph updated with the one or more impactrelationships. In some embodiments, control applications for controllingthe equipment can analyze the impact relationship to meet setpointsand/or other control goals. In some embodiments, one or more agents ofthe space graph database 1120 can assess the impact relationships andupdate control algorithms of the space graph database 1120, update theoperation of the one or more pieces of building equipment.

Configuration of Exemplary Embodiments

The construction and arrangement of the systems and methods as shown inthe various exemplary embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible (e.g., variations in sizes, dimensions,structures, shapes and proportions of the various elements, values ofparameters, mounting arrangements, use of materials, colors,orientations, etc.). For example, the position of elements can bereversed or otherwise varied and the nature or number of discreteelements or positions can be altered or varied. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure. The order or sequence of any process or method stepscan be varied or re-sequenced according to alternative embodiments.Other substitutions, modifications, changes, and omissions can be madein the design, operating conditions and arrangement of the exemplaryembodiments without departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure can be implementedusing existing computer processors, or by a special purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Combinationsof the above are also included within the scope of machine-readablemedia. Machine-executable instructions include, for example,instructions and data which cause a general purpose computer, specialpurpose computer, or special purpose processing machines to perform acertain function or group of functions.

Although the figures show a specific order of method steps, the order ofthe steps may differ from what is depicted. Also two or more steps canbe performed concurrently or with partial concurrence. Such variationwill depend on the software and hardware systems chosen and on designerchoice. All such variations are within the scope of the disclosure.Likewise, software implementations could be accomplished with standardprogramming techniques with rule based logic and other logic toaccomplish the various connection steps, processing steps, comparisonsteps and decision steps.

The term “client or “server” include all kinds of apparatus, devices,and machines for processing data, including by way of example aprogrammable processor, a computer, a system on a chip, or multipleones, or combinations, of the foregoing. The apparatus may includespecial purpose logic circuitry, e.g., a field programmable gate array(FPGA) or an application specific integrated circuit (ASIC). Theapparatus may also include, in addition to hardware, code that createsan execution environment for the computer program in question (e.g.,code that constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, a cross-platform runtimeenvironment, a virtual machine, or a combination of one or more ofthem). The apparatus and execution environment may realize variousdifferent computing model infrastructures, such as web services,distributed computing and grid computing infrastructures.

The systems and methods of the present disclosure may be completed byany computer program. A computer program (also known as a program,software, software application, script, or code) may be written in anyform of programming language, including compiled or interpretedlanguages, declarative or procedural languages, and it may be deployedin any form, including as a stand-alone program or as a module,component, subroutine, object, or other unit suitable for use in acomputing environment. A computer program may, but need not, correspondto a file in a file system. A program may be stored in a portion of afile that holds other programs or data (e.g., one or more scripts storedin a markup language document), in a single file dedicated to theprogram in question, or in multiple coordinated files (e.g., files thatstore one or more modules, sub programs, or portions of code). Acomputer program may be deployed to be executed on one computer or onmultiple computers that are located at one site or distributed acrossmultiple sites and interconnected by a communication network.

The processes and logic flows described in this specification may beperformed by one or more programmable processors executing one or morecomputer programs to perform actions by operating on input data andgenerating output. The processes and logic flows may also be performedby, and apparatus may also be implemented as, special purpose logiccircuitry (e.g., an FPGA or an ASIC).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing actions in accordance with instructions andone or more memory devices for storing instructions and data. Generally,a computer will also include, or be operatively coupled to receive datafrom or transfer data to, or both, one or more mass storage devices forstoring data (e.g., magnetic, magneto-optical disks, or optical disks).However, a computer need not have such devices. Moreover, a computer maybe embedded in another device (e.g., a mobile telephone, a personaldigital assistant (PDA), a mobile audio or video player, a game console,a Global Positioning System (GPS) receiver, or a portable storage device(e.g., a universal serial bus (USB) flash drive), etc.). Devicessuitable for storing computer program instructions and data include allforms of non-volatile memory, media and memory devices, including by wayof example semiconductor memory devices (e.g., EPROM, EEPROM, and flashmemory devices; magnetic disks, e.g., internal hard disks or removabledisks; magneto-optical disks; and CD ROM and DVD-ROM disks). Theprocessor and the memory may be supplemented by, or incorporated in,special purpose logic circuitry.

To provide for interaction with a user, implementations of the subjectmatter described in this specification may be implemented on a computerhaving a display device (e.g., a CRT (cathode ray tube), LCD (liquidcrystal display), OLED (organic light emitting diode), TFT (thin-filmtransistor), or other flexible configuration, or any other monitor fordisplaying information to the user and a keyboard, a pointing device,e.g., a mouse, trackball, etc., or a touch screen, touch pad, etc.) bywhich the user may provide input to the computer. Other kinds of devicesmay be used to provide for interaction with a user as well; for example,feedback provided to the user may be any form of sensory feedback (e.g.,visual feedback, auditory feedback, or tactile feedback), and input fromthe user may be received in any form, including acoustic, speech, ortactile input. In addition, a computer may interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's client device in response to requests received from the webbrowser.

Implementations of the subject matter described in this disclosure maybe implemented in a computing system that includes a back-end component(e.g., as a data server), or that includes a middleware component (e.g.,an application server), or that includes a front end component (e.g., aclient computer) having a graphical user interface or a web browserthrough which a user may interact with an implementation of the subjectmatter described in this disclosure, or any combination of one or moresuch back end, middleware, or front end components. The components ofthe system may be interconnected by any form or medium of digital datacommunication (e.g., a communication network). Examples of communicationnetworks include a LAN and a WAN, an inter-network (e.g., the Internet),and peer-to-peer networks (e.g., ad hoc peer-to-peer networks).

The present disclosure may be embodied in various different forms, andshould not be construed as being limited to only the illustratedembodiments herein. Rather, these embodiments are provided as examplesso that this disclosure will be thorough and complete, and will fullyconvey the aspects and features of the present disclosure to thoseskilled in the art. Accordingly, processes, elements, and techniquesthat are not necessary to those having ordinary skill in the art for acomplete understanding of the aspects and features of the presentdisclosure may not be described. Unless otherwise noted, like referencenumerals denote like elements throughout the attached drawings and thewritten description, and thus, descriptions thereof may not be repeated.Further, features or aspects within each example embodiment shouldtypically be considered as available for other similar features oraspects in other example embodiments.

It will be understood that, although the terms “first,” “second,”“third,” etc., may be used herein to describe various elements,components, regions, layers and/or sections, these elements, components,regions, layers and/or sections should not be limited by these terms.These terms are used to distinguish one element, component, region,layer or section from another element, component, region, layer orsection. Thus, a first element, component, region, layer or sectiondescribed below could be termed a second element, component, region,layer or section, without departing from the spirit and scope of thepresent disclosure.

The terminology used herein is for the purpose of describing particularembodiments and is not intended to be limiting of the presentdisclosure. As used herein, the singular forms “a” and “an” are intendedto include the plural forms as well, unless the context clearlyindicates otherwise. It will be further understood that the terms“comprises,” “comprising,” “includes,” and “including,” “has,” “have,”and “having,” when used in this specification, specify the presence ofthe stated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof. As used herein, the term “and/or” includes anyand all combinations of one or more of the associated listed items.Expressions such as “at least one of,” when preceding a list ofelements, modify the entire list of elements and do not modify theindividual elements of the list.

As used herein, the term “substantially,” “about,” and similar terms areused as terms of approximation and not as terms of degree, and areintended to account for the inherent variations in measured orcalculated values that would be recognized by those of ordinary skill inthe art. Further, the use of “may” when describing embodiments of thepresent disclosure refers to “one or more embodiments of the presentdisclosure.” As used herein, the terms “use,” “using,” and “used” may beconsidered synonymous with the terms “utilize,” “utilizing,” and“utilized,” respectively. Also, the term “exemplary” is intended torefer to an example or illustration.

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

What is claimed is:
 1. A building system for operating a building andmanaging building information, the building system comprising one ormore memory devices configured to store instructions thereon, theinstructions causing one or more processors to: receive, by the one ormore processors, building data from one or more building data sources;generate, by the one or more processors, a temporary relationshipbetween a first entity and a second entity of a space graph, wherein thespace graph is a graph data structure comprising a plurality of nodesrepresenting a plurality of entities, a plurality of edges between theplurality of nodes representing a plurality of relationships between theplurality of entities, and data values of the building data associatedwith the entities; cause, by the one or more processors, the space graphto include the temporary relationship by storing a temporary edgebetween a first node of the plurality of nodes representing the firstentity and a second node of the plurality of nodes representing thesecond entity; perform, by the one or more processors, one or morecontrol operations based on the space graph including the temporaryedge; receive, by the one or more processors, new building data from theone or more building data sources; determine, by the one or moreprocessors, whether to generate a permanent relationship to replace thetemporary relationship based on the new building data; and update, bythe one or more processors, the space graph by causing the permanentrelationship to replace the temporary relationship of the space graph inresponse to a determination to generate the permanent relationship toreplace the temporary relationship by causing a permanent edge toreplace the temporary edge.
 2. The building system of claim 1, whereinthe instructions cause the one or more processors to determine, by theone or more processors, whether to generate a permanent relationship toreplace the temporary relationship by: determining a confidence levelfor the permanent relationship; replacing the temporary relationshipwith the permanent relationship in response to a determination that theconfidence level is greater than a predefined amount.
 3. The buildingsystem of claim 1, wherein the temporary relationship is a singlerelationship, wherein the permanent relationship comprises a firstrelationship between the first entity and the second entity and a secondrelationship between the second entity and the first entity.
 4. Thebuilding system of claim 1, wherein the instructions cause the one ormore processors to: receive, by the one or more processors, a query forinformation of the space graph from a requesting device, wherein theinformation is included by one of the plurality of nodes of the spacegraph; retrieve, by the one or more processors, the information from thespace graph by traversing at least some of the plurality of entities andat least some of the plurality of edges to identify the informationwithout traversing other entities or other relationships of a datastructure other than the space graph; and provide, by the one or moreprocessors, the information to the requesting device.
 5. The buildingsystem of claim 4, wherein the query comprises an indication of the atleast some of the plurality of nodes and the at least some of theplurality of entities to traverse to identify the information.
 6. Thebuilding system of claim 1, wherein the instructions cause the one ormore processors to generate, by the one or more processors, based on thenew building data, the temporary relationship between the first entityof the plurality of entities and the second entity of the plurality ofentities by: determining whether a plurality of events are triggered byanalyzing a plurality of rules with the new building data, wherein eachof the plurality of events is associated with one of the plurality ofrules; and determining, based on a pattern of the plurality of eventsthat are triggered, the new relationship.
 7. The building system ofclaim 6, wherein determining, based on the pattern of the plurality ofevents that are triggered, the new relationship comprises determiningwhether a number of the plurality of events that are triggered isgreater than a predefined amount.
 8. The building system of claim 6,wherein each of the plurality of rules is a conditional rule based onwhether operational data of the plurality of entities exists and that atleast some of the plurality of relationships exist, wherein the newbuilding data is the operational data.
 9. A method for a building systemof a building, the method comprising: receiving, by a processingcircuit, building data from one or more building data sources;generating, by the processing circuit, a temporary relationship betweena first entity and a second entity of a space graph, wherein the spacegraph is a graph data structure comprising a plurality of nodesrepresenting a plurality of entities, a plurality of edges between theplurality of nodes representing a plurality of relationships between theplurality of entities, and data values of the building data associatedwith the entities; causing, by the processing circuit, the space graphto include the temporary relationship by storing a temporary edgebetween a first node of the plurality of nodes representing the firstentity and a second node of the plurality of nodes representing thesecond entity; performing, by the processing circuit, one or morecontrol operations based on the space graph including the temporaryedge; receiving, by the processing circuit, new building data from theone or more building data sources; determining, by the processingcircuit, whether to generate a permanent relationship to replace thetemporary relationship based on the new building data; and updating, bythe processing circuit the space graph by causing the permanentrelationship to replace the temporary relationship of the space graph inresponse to a determination to generate the permanent relationship toreplace the temporary relationship by causing a permanent edge toreplace the temporary edge.
 10. The method of claim 9, whereindetermining, by the processing circuit, whether to generate a permanentrelationship to replace the temporary relationship comprises:determining a confidence level for the permanent relationship; andreplacing the temporary relationship with the permanent relationship inresponse to a determination that the confidence level is greater than apredefined amount.
 11. The method of claim 9, wherein the temporaryrelationship is a single relationship, wherein the permanentrelationship comprises a first relationship between the first entity andthe second entity and a second relationship between the second entityand the first entity.
 12. The method of claim 9, further comprising:receiving, by the processing circuit, a query for information of thespace graph from a requesting device, wherein the information isincluded by one of the plurality of nodes of the space graph;retrieving, by the processing circuit, the information from the spacegraph by traversing at least some of the plurality of entities and atleast some of the plurality of edges to identify the information withouttraversing other entities or other relationships of a data structureother than the space graph; and providing, by the processing circuit,the information to the requesting device.
 13. The method of claim 12,wherein the query comprises an indication of the at least some of theplurality of nodes and the at least some of the plurality of entities totraverse to identify the information.
 14. The method of claim 12,wherein generating, by the processing circuit, based on the new buildingdata, the temporary relationship between the first entity of theplurality of entities and the second entity of the plurality of entitiescomprises: determining whether a plurality of events are triggered byanalyzing a plurality of rules with the new building data, wherein eachof the plurality of events is associated with one of the plurality ofrules; and determining, based on a pattern of the plurality of eventsthat are triggered, the new relationship.
 15. The method of claim 14,wherein determining, based on the pattern of the plurality of eventsthat are triggered, the new relationship comprises determining whether anumber of the plurality of events that are triggered is greater than apredefined amount.
 16. The method of claim 14, wherein each of theplurality of rules is a conditional rule based on whether operationaldata of the plurality of entities exists and that at least some of theplurality of relationships exist, wherein the new building data is theoperational data.
 17. A building management system for operating abuilding and managing building information, the building managementsystem comprising a processing circuit configured to: receive, by theprocessing circuit, building data from one or more building datasources; generate, by the processing circuit, a temporary relationshipbetween a first entity and a second entity of a space graph, wherein thespace graph is a graph data structure comprising a plurality of nodesrepresenting a plurality of entities, a plurality of edges between theplurality of nodes representing a plurality of relationships between theplurality of entities, and data values of the building data associatedwith the entities; cause, by the processing circuit, the space graph toinclude the temporary relationship by storing a temporary edge between afirst node of the plurality of nodes representing the first entity and asecond node of the plurality of nodes representing the second entity;perform, by the processing circuit, one or more control operations basedon the space graph including the temporary edge; receive, by theprocessing circuit, new building data from the one or more building datasources; determine, by the processing circuit, whether to generate apermanent relationship to replace the temporary relationship based onthe new building data; and update, by the processing circuit, the spacegraph by causing the permanent relationship to replace the temporaryrelationship of the space graph in response to a determination togenerate the permanent relationship to replace the temporaryrelationship by causing a permanent edge to replace the temporary edge.18. The building management system of claim 17, wherein the processingcircuit is configured to determine, by the processing circuit, whetherto generate a permanent relationship to replace the temporaryrelationship comprises: determining a confidence level for the permanentrelationship; and replacing the temporary relationship with thepermanent relationship in response to a determination that theconfidence level is greater than a predefined amount.
 19. The buildingmanagement system of claim 17, wherein the temporary relationship is asingle relationship, wherein the permanent relationship comprises afirst relationship between the first entity and the second entity and asecond relationship between the second entity and the first entity. 20.The building management system of claim 17, wherein the processingcircuit is configured to: receive, by the processing circuit, a queryfor information of the space graph from a requesting device, wherein theinformation is included by one of the plurality of nodes of the spacegraph; retrieve, by the processing circuit, the information from thespace graph by traversing at least some of the plurality of entities andat least some of the plurality of edges to identify the informationwithout traversing other entities or other relationships of a datastructure other than the space graph; and provide, by the processingcircuit, the information to the requesting device.